{"as_of":"2026-08-18T16:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d19698e8068c5a1ffafd3ccaf0aadf45e5808ea228315eba7b2b98e1e4db1b17","coverage":[{"denominator":300,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:28:36.988660Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.18915/citation-record","integrity":"/paper/2506.18915/integrity","json":"/paper/2506.18915/citation-record.json","paper":"/paper/2506.18915"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:28.883321Z","title":"Depression in young people,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:28.883321Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:20385eb5e34eb1bdca0536ec6a1ec4274b9ee5b7f21128b7c1aeb0c87db50918","observation_id":"ce241d4f-6b32-4e7e-a325-c9f3d1dd4d0c","resolution":{"observed_at":"2026-08-07T05:28:28.883321Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:28.939562Z","title":"Paral- imbic cortical thickness in first-episode depression: evidence for trait-related differences in mood regulation,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:28.939562Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:d26e6c7047ca3d79c7b3b9cb0d9d735866368e7418f0c8c9968949ab46dcbd61","observation_id":"618db548-fcc2-4b67-8601-8a62cf59fd59","resolution":{"observed_at":"2026-08-07T05:28:28.939562Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:29.032581Z","title":"Im- paired prefrontal–amygdala effective connectivity is responsible for the dysfunction of emotion process in major depressive dis- order: a dynamic causal modeling study on meg,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.032581Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:5c8b9c41a9e7bc49fe336582b776d4a485ad9fa006c40002f30ae13d0f5fd5f8","observation_id":"cc151ca2-8093-4357-b557-6bc252c8bf3f","resolution":{"observed_at":"2026-08-07T05:28:29.032581Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:29.131675Z","title":"Processing of facial emo- tion expression in major depression: a review,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.131675Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:ac19b5f9a76cc9b9d54f263b164b11149dfc174bad02a11f5d55edf10aba99ed","observation_id":"cd6d4419-a52a-4117-8dde-d264ee3e37b4","resolution":{"observed_at":"2026-08-07T05:28:29.131675Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:29.211559Z","title":"Depression and the risk of coronary heart disease: a meta-analysis of prospective cohort studies,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.211559Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:d7425c335c9a99c45000891d0b0e24326fcd380ac50dfe0fe597814a555b9b96","observation_id":"16b2be80-ff39-4db8-9253-241ef51bb4b6","resolution":{"observed_at":"2026-08-07T05:28:29.211559Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:29.298289Z","title":"The increasing burden of depression,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.298289Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:912e04a80fb5695abcc2139930f55dca01749240bcece5a554862ab33601dc71","observation_id":"114e6699-b728-4dfe-ad0d-ac20943a8cf5","resolution":{"observed_at":"2026-08-07T05:28:29.298289Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:29.348356Z","title":"Chronic fatigue syndrome and depression: cause, effect, or covariate,","venue":null,"work_id":null,"year":1991},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.348356Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:f4ebe185aa4dd3dfe93fe4d551b37b84505d64252e159e07e8724d21d6949df8","observation_id":"fcd1f59d-f89c-406c-87fe-6bc2513b7025","resolution":{"observed_at":"2026-08-07T05:28:29.348356Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:29.408500Z","title":"Depression and appetite,","venue":null,"work_id":null,"year":1977},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.408500Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:ce72594ab85e2592fb5fb60a9098c6b4b52ddb6f61dfc1e5e9c9b3314a9ef249","observation_id":"2b9758c6-0b36-4f54-b456-f2b43fe73a2a","resolution":{"observed_at":"2026-08-07T05:28:29.408500Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:29.483776Z","title":"Insomnia and depression,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.483776Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:339d620049c076a831acdee2805a1131bfb2e456f855dc945d587d79a59fdd80","observation_id":"7ab8adf7-cce0-4d4a-a727-67db6b3c259e","resolution":{"observed_at":"2026-08-07T05:28:29.483776Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:29.577139Z","title":"The diagnosis of depression: current and emerging methods,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.577139Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:75c44cf9a376d1a2970d4a627ba0a9a5fee77c9fdd5d5f1a6aa396719aff20ee","observation_id":"e676e7bf-00b0-4cda-8f27-d12b06c37bf7","resolution":{"observed_at":"2026-08-07T05:28:29.577139Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:29.657600Z","title":"A rating scale for depression. journal of neurol- ogy,","venue":null,"work_id":null,"year":1960},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.657600Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:5f124436fcecfaca14e16bdb12bafdb1ee7fe7829c727cf6dfe936b0c146b331","observation_id":"885e76dd-e2f1-4dbe-b259-f84e0b926ee5","resolution":{"observed_at":"2026-08-07T05:28:29.657600Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:29.740409Z","title":"The phq-9: valid- ity of a brief depression severity measure,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.740409Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:c42fb0e53fe6574fc68043ceb224bc75b02edec2462ec2de7f314c5ff5770ad3","observation_id":"fea35e8c-ad84-4c2b-9f00-700b6a481fbc","resolution":{"observed_at":"2026-08-07T05:28:29.740409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:29.804288Z","title":"Adolescent depression: diagnosis, treatment, and educational attainment,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.804288Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:3c82f2efb62b4f7ab981d8167123b9580f319237e7f8361dff7a11751ea38b7f","observation_id":"58ad0241-4d17-4854-a79d-738def413eef","resolution":{"observed_at":"2026-08-07T05:28:29.804288Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:29.895983Z","title":"Depression gets old fast: do stress and depression accelerate cell aging?","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.895983Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:b99d24f0d1d7dac422bfc646dc5a04e5a303f6d17b6b3d9bbdb9efabe89604b2","observation_id":"bdc2824b-e36c-48b5-95ac-329a99e59fdb","resolution":{"observed_at":"2026-08-07T05:28:29.895983Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:29.979447Z","title":"Depression and obesity: evidence of shared biological mechanisms,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.979447Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:dab3a1f6a218e404de94cd7bb5b2c901cf5fa4aa4fc9f083ddb7305e55e29cc7","observation_id":"69fab345-2f7d-4352-afc4-fafedbad7649","resolution":{"observed_at":"2026-08-07T05:28:29.979447Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:30.042365Z","title":"Shared biological mechanisms of depression and obesity: focus on adipokines and lipokines,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:30.042365Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:e3a0011d24067a69ef81f480adfd07b60f7877d35eb6d54879a34307f2374e5c","observation_id":"59c55522-af6f-460e-a15f-5554eb0d7b02","resolution":{"observed_at":"2026-08-07T05:28:30.042365Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:30.111666Z","title":"Major depression: an illness with objective physical signs,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:30.111666Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:c7a6270ab14b4ce6d748ab0c773a5ef7474a54bdd57d2dd09d2d617282f39f5d","observation_id":"09449afc-331f-4d51-85a3-77a1da187adc","resolution":{"observed_at":"2026-08-07T05:28:30.111666Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:30.184864Z","title":"A study of the interaction between depressed patients and their spouses,","venue":null,"work_id":null,"year":1975},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:30.184864Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:4010593f462c0e870b8552ee923b5398d00dc43d7310ce71e4693e299b94f794","observation_id":"dfdb2123-0a1a-4559-afec-367b62e54cd3","resolution":{"observed_at":"2026-08-07T05:28:30.184864Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:30.253853Z","title":"From emotions to mood disorders: A survey on gait analysis methodology,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:30.253853Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:62bc917fa8f4db7b7f85ab0234d8df196672f47c880e9ec81fbc0dd89adc862c","observation_id":"fe5f0ce1-2b6e-4574-a5b8-4e20066f40fe","resolution":{"observed_at":"2026-08-07T05:28:30.253853Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:30.344544Z","title":"Reading between the frames: Multi- modal depression detection in videos from non-verbal cues,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:30.344544Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:19d61f48aa495492434b0b5c2939243590123f0983457ed92badae84989caa24","observation_id":"2bce2f39-9e0d-4438-a307-ed442b1ad25f","resolution":{"observed_at":"2026-08-07T05:28:30.344544Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:30.409859Z","title":"Mea- suring the rate of change of voice fundamental frequency in fluent speech during mental depression,","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:30.409859Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:36bb2dd2bc802564e92148171374367a353c359ab922faec94af42e28fc0809d","observation_id":"aa3cbfa2-1dc8-4dda-bf1e-cc343c5ffa58","resolution":{"observed_at":"2026-08-07T05:28:30.409859Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:30.489713Z","title":"Linguistic in- quiry and word count: Liwc 2001,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:30.489713Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:af379e83bf532dba0a735b9e2d083b960eb769dbf76e60d4741fac42f353f1d5","observation_id":"42667e1e-930c-4437-aba2-7d69681bd7f8","resolution":{"observed_at":"2026-08-07T05:28:30.489713Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:30.541155Z","title":"Vocal indicators of psychological stress,","venue":null,"work_id":null,"year":1980},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:30.541155Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:a55154488bda6518534175b3ee19c47b1ff18957738c9f8fec002dffd8d22644","observation_id":"99d43c60-c379-4378-b0cb-4b0df087d69c","resolution":{"observed_at":"2026-08-07T05:28:30.541155Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:30.603541Z","title":"Ensemble cca for continuous emotion prediction,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:30.603541Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:659868a808a947833e801999565adabbb64a461c32fcf4908d907374759fe451","observation_id":"db9403ad-bf7b-4817-8e62-500da39edc9f","resolution":{"observed_at":"2026-08-07T05:28:30.603541Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:30.676037Z","title":"Depression assessment by fusing high and low level features from audio, video, and text,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:30.676037Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:7b0e0ff865c5fb34fdd55290b197ef345f5e63bd64c439fecd01b4b207e401e4","observation_id":"27f04710-2634-425e-9192-60fc906125bf","resolution":{"observed_at":"2026-08-07T05:28:30.676037Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:30.739474Z","title":"End-to-end mul- timodal clinical depression recognition using deep neural networks: A comparative analysis,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:30.739474Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:3373dfc156339b2bb8bde8b535336b6130fe4926a2118b883b6c4c3c4a88771d","observation_id":"a9fabc5a-4f2a-40f1-bd39-3ef8541d8b51","resolution":{"observed_at":"2026-08-07T05:28:30.739474Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:30.804713Z","title":"Hique: Hierarchical question embedding network for multimodal depression detec- tion,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:30.804713Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:c98354dc9cb6221882fa535e67911732a9e6a31f7e6c09ad3b1938815fd35a8d","observation_id":"3ec7b258-aae3-4029-af77-c0c59fcf171e","resolution":{"observed_at":"2026-08-07T05:28:30.804713Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:30.881684Z","title":"Two- stage temporal modelling framework for video-based depression recognition using graph representation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:30.881684Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:6559fd84248013d1499f1cb442bbe169439de8465b56aa41eadf1439ec5352bd","observation_id":"aa3a3fd2-ab74-4c8a-94bf-bda4e3acab6f","resolution":{"observed_at":"2026-08-07T05:28:30.881684Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:30.949838Z","title":"Multimodal spatiotemporal representation for automatic depression level de- tection,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:30.949838Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:65481805afdc5f65fd144ea34f4b3d93228be2c21584c014d473be88c25d23be","observation_id":"6fafaab5-71f7-49a3-9910-feb54468f55f","resolution":{"observed_at":"2026-08-07T05:28:30.949838Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:31.017303Z","title":"Depression detection from social media posts using emotion aware encoders and fuzzy based contrastive networks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:31.017303Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:1bed991ca1619f6d682e4305170f2c6bf8f8cdcdcf722e7918725efd56138223","observation_id":"a2c9cea2-ba18-4bed-9e0f-384ab75627e6","resolution":{"observed_at":"2026-08-07T05:28:31.017303Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:31.102361Z","title":"Dep-former: Multimodal depression recognition based on facial expressions and audio features via emotional changes,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:31.102361Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:d3220b4f5f01922b932f1dc9cb539567dc5b9f6e74a36d7335ea9a1f22ea9f7c","observation_id":"a89848dc-62ac-4133-b0b8-4afcd284d9ef","resolution":{"observed_at":"2026-08-07T05:28:31.102361Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:31.225969Z","title":"A mul- timodal fusion model with multi-level attention mechanism for depression detection,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:31.225969Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:39272570e2f644678a90c5dfe0a16ecad88f89142dbdb9210f91e5ee73208bfc","observation_id":"80648e0b-5bc7-417d-ae14-809ec2d8873f","resolution":{"observed_at":"2026-08-07T05:28:31.225969Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:31.381218Z","title":"Explainable depression detection via head motion patterns,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:31.381218Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:0d52ba96462ce1c0f9b7b5a19b92dcdbbfe6ff3403e5936c89fbefb035057650","observation_id":"35a719a6-07d3-4d20-83fb-2b56552391f3","resolution":{"observed_at":"2026-08-07T05:28:31.381218Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:31.471608Z","title":"Statistical, spectral and graph representations for video-based facial expression recogni- tion in children,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:31.471608Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:8d4a16b3d591e1ae4b84c726542d10c50421e86721a62860c43d2a104b9acd2b","observation_id":"f3883801-4a60-46b9-bbd3-c53188b39716","resolution":{"observed_at":"2026-08-07T05:28:31.471608Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:31.548751Z","title":"Integrat- ing deep facial priors into landmarks for privacy preserving mul- timodal depression recognition,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:31.548751Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:f353f17c19c97d58a85aa677e5d6a8dfa69e99d6a1db66e992c7707fc6dece81","observation_id":"19fe1eb1-7876-4951-bb11-6901395befad","resolution":{"observed_at":"2026-08-07T05:28:31.548751Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:31.607231Z","title":"Topic modeling based multi-modal depression detection,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:31.607231Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:1b21b9afd77d68f3108c2438b46eb4f95ff2b655440c8bfedc9be9c3aa4f7e2c","observation_id":"767b8426-e9b2-4680-8cfe-350d4d74f23e","resolution":{"observed_at":"2026-08-07T05:28:31.607231Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:31.688696Z","title":"Multimodal fusion of bert-cnn and gated cnn representations for depression detection,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:31.688696Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:36510daf97e39a347ad977232148eaed298365d9e5246849f58e38e4a1e6ba09","observation_id":"d7e3e351-af39-4023-a884-4af251c7c19c","resolution":{"observed_at":"2026-08-07T05:28:31.688696Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:31.773951Z","title":"Facialpulse: An efficient rnn-based de- pression detection via temporal facial landmarks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:31.773951Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:f72e8bc4bd91b434509cd529b9ff4c87631a1ea6dff6d2b7c1b595a20b79c701","observation_id":"ed06f4ee-b63f-4147-a467-fafd8e4bc6f1","resolution":{"observed_at":"2026-08-07T05:28:31.773951Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:31.857172Z","title":"Social risk and depression: Evidence from manual and automatic facial expression analysis,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:31.857172Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:4bfdf9a16fb7a1e0acc75eca217f72888f0e450b5120b6add09fdc1d1f0ce6f9","observation_id":"f89aed52-b3d4-409d-97fc-24a667f06420","resolution":{"observed_at":"2026-08-07T05:28:31.857172Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:31.947424Z","title":"Cnn depression severity level estimation from upper body vs. face-only images,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:31.947424Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:59c95c63b77abda3dd7d9ca494d076f6f73bac1485592846445fbca0190b1374","observation_id":"dca7ac68-baa4-4cd0-8888-072a07b752d3","resolution":{"observed_at":"2026-08-07T05:28:31.947424Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:32.039934Z","title":"Lqgdnet: A local quaternion and global deep network for facial depression recognition,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:32.039934Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:89b05f9cfca6bae21dcc3dfa3cdbb8070707263e924541155ab0e6356f8a5f1a","observation_id":"544d583f-f811-4dba-9de5-9b6e18576206","resolution":{"observed_at":"2026-08-07T05:28:32.039934Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:32.173240Z","title":"Spectral repre- sentation of behaviour primitives for depression analysis,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:32.173240Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:b2a67a79328c5c04e27fdf26fe870b6ee04d527b21673f9a52cb7f02e293532a","observation_id":"2eedef23-2811-4309-aa33-ac1bb529779d","resolution":{"observed_at":"2026-08-07T05:28:32.173240Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:32.238794Z","title":"Mdn: A deep maximization-differentiation network for spatio-temporal de- pression detection,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:32.238794Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:aa2ab0921c0abdb24df1b1ffa64468f749fa9ab20686efcf10c2eefbcbc27700","observation_id":"0651b2b9-dccd-4416-befb-46212222b446","resolution":{"observed_at":"2026-08-07T05:28:32.238794Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:32.298084Z","title":"A novel eeg-based graph convolution network for depression detection: incorporating secondary subject partitioning and attention mech- anism,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:32.298084Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:2378c97612c85b48fc190cd140018bb370e9e79108d9447ca780217e8cfbd4f0","observation_id":"10c09a96-bb0b-49a9-a24b-ffb142c126a3","resolution":{"observed_at":"2026-08-07T05:28:32.298084Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:32.382185Z","title":"Depression recognition from eeg signals using an adaptive channel fusion method via improved focal loss,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:32.382185Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:4291c353a4ca20a82b4a305fd0a5a4ad7eb0e3d5db820ddba979c07db8eeca35","observation_id":"5fecd28d-bd5d-4abb-ad70-98c0fae1ebe3","resolution":{"observed_at":"2026-08-07T05:28:32.382185Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:32.465970Z","title":"Convolutional neural network– based deep learning model for predicting differential suicidality in depressive patients using brain generalized q-sampling imag- ing,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:32.465970Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:04edfd3dec4738f48879996dcb547d910acab3da56ebb40b9f7c9d286191d911","observation_id":"01e42070-cbc1-45b2-956a-5551fe313b8d","resolution":{"observed_at":"2026-08-07T05:28:32.465970Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:32.532866Z","title":"Classification of major depressive disorder using an attention-guided unified deep convolutional neural network and individual structural covariance network,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:32.532866Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:54ec883ac12e30ed6c2d54fbf1fbd0156013897a8efc3a6de63b2d76aa07334c","observation_id":"fb2bc121-2ced-46c3-a251-144325daa99d","resolution":{"observed_at":"2026-08-07T05:28:32.532866Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:32.608209Z","title":"Depression detection from smri and rs-fmri images using machine learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:32.608209Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:11be56ee5eace2bf76eec66f6ef1ff1c96ade1d6df340df0259d727cfd205879","observation_id":"7c5b6a71-ee1c-4351-874c-041588d88c6f","resolution":{"observed_at":"2026-08-07T05:28:32.608209Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:32.693703Z","title":"Depression detection based on deep distribution learning,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:32.693703Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:d7390ece3d15c55d3ad2052021a68b05de0bc6162dbcd2d873efa8ce24235250","observation_id":"598afe1e-6dd0-4812-8642-8e25db94bd03","resolution":{"observed_at":"2026-08-07T05:28:32.693703Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:32.763265Z","title":"Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from eeg signal,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:32.763265Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:aae161db634e5b5b1b2cee93d37922aebe2c6b061159cc5da965e16500e78cde","observation_id":"5090f97d-b938-4890-97b0-da4e9da39f7b","resolution":{"observed_at":"2026-08-07T05:28:32.763265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:32.814814Z","title":"Deprnet: A deep convolution neural network frame- work for detecting depression using eeg,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:32.814814Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:b4705e97369bb73ccd8e9abb8c909c540a646dd55a2e87ac79388c3a828aff48","observation_id":"dcde242f-a84a-4935-a113-1763e4ab1f03","resolution":{"observed_at":"2026-08-07T05:28:32.814814Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:32.899385Z","title":"Depcap: a smart healthcare framework for eeg based depres- sion detection using time-frequency response and deep neural network,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:32.899385Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:732b0ab5236557f64d4ceb0263a1260e1cbedc34ab4f7630eb0c050d2aab7dfb","observation_id":"1bb7af5c-ec53-4f40-9a27-bd4284e13801","resolution":{"observed_at":"2026-08-07T05:28:32.899385Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:32.991626Z","title":"Achieving eeg- based depression recognition using decentralized-centralized structure,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:32.991626Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:d4cc3b5d111cf828d367b1d75db4ac38712e4ba1c33aac7c115631c64dc96bd1","observation_id":"5dda2355-90df-42cd-9e79-a1268bbaae89","resolution":{"observed_at":"2026-08-07T05:28:32.991626Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:33.066886Z","title":"Gctnet: a graph convolutional transformer network for major depressive disorder detection based on eeg signals,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:33.066886Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:0795bb72a275baf0544f0bba395fbab468c03ee5ad9a391586009c4c2dde90e3","observation_id":"592b55f0-eaa4-4179-8a58-5baa2b894ce6","resolution":{"observed_at":"2026-08-07T05:28:33.066886Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:33.130429Z","title":"Mast-gcn: Multi-scale adaptive spatial-temporal graph convolutional network for eeg- based depression recognition,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:33.130429Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:ee574e67b8be70bc1eb83d55c8eb69f4e627c43b94760caece5f2b8b42e570d7","observation_id":"91c2b22e-e5cd-4ff8-b5a7-0c8176c77120","resolution":{"observed_at":"2026-08-07T05:28:33.130429Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:33.190865Z","title":"Gcns–fsmi: Eeg recognition of mental illness based on fine-grained signal features and graph mutual information maximization,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:33.190865Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:1212e4a4dbe4318085e2994941c2bab425afee78c4fba0ba62c4a04192e553b2","observation_id":"24db5a63-857b-4721-8d98-84c1b27b7e31","resolution":{"observed_at":"2026-08-07T05:28:33.190865Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:33.282095Z","title":"Classification of recurrent major depressive disorder using a new time series feature extraction method through multisite rs-fmri data,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:33.282095Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:8f6b95af5c520ae7c5f3b0d8b9bd0ca04b2f4ff18867107af0362534672c6a8a","observation_id":"4a536206-c8e8-425c-a266-946653c14630","resolution":{"observed_at":"2026-08-07T05:28:33.282095Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:33.353435Z","title":"The classification of brain network for major depressive disorder patients based on deep graph con- volutional neural network,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:33.353435Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:56e1f9e051bbc2eaf1573b3a9fd2a05664c02fa3fc1980a56f7cae0fe53c2cf9","observation_id":"236350ca-c083-41fb-8ea3-665b3fc9c91b","resolution":{"observed_at":"2026-08-07T05:28:33.353435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:33.437679Z","title":"Avec 2013: the continuous audio/visual emotion and depression recognition challenge,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:33.437679Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:31633ddee7601143cd9433d8287118a93ebc01b0c414b7aedbd91c6cda86e96e","observation_id":"01e45470-26b8-4591-8379-0f39a4bdb8dc","resolution":{"observed_at":"2026-08-07T05:28:33.437679Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:33.499150Z","title":"Avec 2014: 3d dimensional affect and depression recognition challenge,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:33.499150Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:add31c2a1d6615fe343a6cf6b02656697da0e9b02440783c17aff676fbbd04bd","observation_id":"f2607da9-689e-4141-8b96-cac49483bdac","resolution":{"observed_at":"2026-08-07T05:28:33.499150Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:33.585265Z","title":"Avec 2016: Depression, mood, and emotion recognition workshop and challenge,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:33.585265Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:6cebdb3d2efc8ab39edaf273bf6d1773ef1500a8d63a7c9d10dc2672bec06e37","observation_id":"c66fba03-0cac-480c-8365-306f473c30b9","resolution":{"observed_at":"2026-08-07T05:28:33.585265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:33.659123Z","title":"Avec 2017: Real-life depression, and affect recognition workshop and challenge,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:33.659123Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:6fccc5a751f678807528db5021f68f2c9a0981c015e4b961c0f043630a9dbe7c","observation_id":"6826defc-f827-44a3-80fd-ad8667e315a2","resolution":{"observed_at":"2026-08-07T05:28:33.659123Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:33.737173Z","title":"Avec 2019 workshop and challenge: State-of-mind, detecting depression with ai, and cross-cultural affect recognition,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:33.737173Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:c05fca1a14af30da594a6a86adfd691e9386f26772b5ccab2b9bc41297772ab2","observation_id":"efb38aa7-5e28-4b15-886f-19b036374349","resolution":{"observed_at":"2026-08-07T05:28:33.737173Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:33.808080Z","title":"The distress analysis interview corpus of human and computer interviews","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:33.808080Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:f7f2ca239da0f6b71b7e493bfd1f12c5fab503a9dbcc7337325d56a89b1db4a6","observation_id":"9325a99e-09c7-4fd3-9534-54119f9d0380","resolution":{"observed_at":"2026-08-07T05:28:33.808080Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:33.910997Z","title":"Semi-structural interview-based chinese multimodal de- pression corpus towards automatic preliminary screening of de- pressive disorders,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:33.910997Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:0cfa11eae170f4887cebf425719a287ac259aeaab4a79b33026c6e24d2929387","observation_id":"dd201a14-4bff-4397-8c47-eae6c1edbc91","resolution":{"observed_at":"2026-08-07T05:28:33.910997Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:33.988059Z","title":"Dynamic multi- modal measurement of depression severity using deep autoen- coding,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:33.988059Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:328db0aa98bd7f49fe036c9138b7e2bd30243876ff62b62bb4a0e0c5c1227f3b","observation_id":"f34b0a93-d107-4432-b6e5-fd76f377b974","resolution":{"observed_at":"2026-08-07T05:28:33.988059Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:34.075560Z","title":"Look- ing at the body: Automatic analysis of body gestures and self-adaptors in psychological distress,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:34.075560Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:cb13f2cfd558cec09e548b5d6b3fc3244543c0542003d341040fd7d317ae3ff5","observation_id":"404a1594-4c94-4d1a-8363-9f084b1c8dc9","resolution":{"observed_at":"2026-08-07T05:28:34.075560Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:34.167254Z","title":"D-vlog: Multimodal vlog dataset for depression detection,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:34.167254Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:f39eb798d1cf7055b48d62d77f2383a0d38396b73e2537de60ee558694d2e0e2","observation_id":"e7eb5be0-a492-41cf-913c-b321ae0863f7","resolution":{"observed_at":"2026-08-07T05:28:34.167254Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:34.296148Z","title":"Automatic depression detec- tion: An emotional audio-textual corpus and a gru/bilstm-based model,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:34.296148Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:b13d73fa2738dbf0962a272662e24136c15f3f00a99a2c380e0c8cdd27d3d9d6","observation_id":"dc58753c-fa5e-46a3-b709-08a50e117d31","resolution":{"observed_at":"2026-08-07T05:28:34.296148Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:34.359811Z","title":"A multi-modal open dataset for mental- disorder analysis,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:34.359811Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:fd0f6741531de2e23812253a123c46397eb7075dd2951f908f147dc437795b7d","observation_id":"2173d66b-92b1-4826-96ad-192c1223e70e","resolution":{"observed_at":"2026-08-07T05:28:34.359811Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:34.445917Z","title":"Machine learning in major depression: From classification to treatment outcome prediction,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:34.445917Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:e03c22398375637620bb181c8137a06facdf92d1e2d9687ac959fb0f7b20b89a","observation_id":"664d030b-8004-4ef6-920e-c38ba77a6f1e","resolution":{"observed_at":"2026-08-07T05:28:34.445917Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:34.496315Z","title":"Deep learning for depres- sion recognition with audiovisual cues: A review,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:34.496315Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:ddcf7ece5245d45792cdf813ca413b97600d5aa5ace01900136cad241f008076","observation_id":"07e1881b-9d6c-46de-9b09-889bb45becbb","resolution":{"observed_at":"2026-08-07T05:28:34.496315Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:34.582982Z","title":"Machine learning algorithms for depression: diagnosis, insights, and research directions,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:34.582982Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:3ffca95c837c4c9b8eec7033475781dee853acefc63d8e38647423083fb0574b","observation_id":"7e7bdb56-272c-4821-a739-d7727c767ddf","resolution":{"observed_at":"2026-08-07T05:28:34.582982Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:34.674231Z","title":"Auto- matic depression recognition by intelligent speech signal pro- cessing: A systematic survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:34.674231Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:f5f0ec43fae9387dede6240fcc12c60f0ab7305b92d29b8d559c2b50dc5ca685","observation_id":"3722afca-b9f9-4633-9f5b-b767262538c1","resolution":{"observed_at":"2026-08-07T05:28:34.674231Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:34.797699Z","title":"Deep learning and machine learning in psychiatry: a survey of current progress in depression detection, diagnosis and treatment,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:34.797699Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:d32781e98e66b0c257e0883e946bdc8976c63a208be99653062f55c687c480c2","observation_id":"ec53ea1a-3a35-4a5e-bdf5-296545f241fd","resolution":{"observed_at":"2026-08-07T05:28:34.797699Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:34.872146Z","title":"Depression detection from social networks data based on machine learning and deep learning techniques: An interrogative survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:34.872146Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:f0aa310e538196ca20666842bc14f4ab2531e214563ac07605ed9853f8db1acf","observation_id":"50a165be-410b-43f4-9852-e4f26eede92b","resolution":{"observed_at":"2026-08-07T05:28:34.872146Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:34.918526Z","title":"Can’t shake that feeling: event-related fmri assessment of sustained amygdala activity in response to emotional infor- mation in depressed individuals,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:34.918526Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:f22b9f9c6605ed139e63fd73076f2ce7647990343cad5fececbd51a4d0a41007","observation_id":"ad3bd739-8f8a-41e4-b3b7-f9027f06e4cb","resolution":{"observed_at":"2026-08-07T05:28:34.918526Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:35.021392Z","title":"The thalamus is the causal hub of intervention in patients with major depressive disorder: Evidence from the granger causality analysis,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:35.021392Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:5bdae0a714e865b66c42fc99b6cb62d6a7759a0f62e192774f513381a6091ddc","observation_id":"c90c8015-3691-4c9e-a5a0-0f2b65ba7224","resolution":{"observed_at":"2026-08-07T05:28:35.021392Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:35.089937Z","title":"Neuroplastic changes in depression: a role for the immune system,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:35.089937Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:3b43e363f77da3c6c0f0eeff643e50766be1e9df67c8210c41205edecab606cc","observation_id":"d38d65a2-71b8-42f8-9b55-0f2b4d5f8b5d","resolution":{"observed_at":"2026-08-07T05:28:35.089937Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:35.202903Z","title":"Illness, cytokines, and depression,","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:35.202903Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:52920020961c5dfd4768ceec25dd865db909aeea52305a065c6e676f938d493d","observation_id":"750a500c-bcd3-44b4-8a24-cfd6fc98d911","resolution":{"observed_at":"2026-08-07T05:28:35.202903Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:35.270405Z","title":"The association of depres- sion and anxiety with medical symptom burden in patients with chronic medical illness,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:35.270405Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:7b4b06fa28463ab8e09356e38a1e8d2e683e80a7e2ac0808f9a32776690776ea","observation_id":"f6b0cacc-872b-493c-b069-bdc4bfab3d92","resolution":{"observed_at":"2026-08-07T05:28:35.270405Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:35.345952Z","title":"Depressive behavior and vascular dysfunction: a link between clinical depression and vascular disease?","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:35.345952Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:50931816cf68123e2b58e21a254cb3a88bbcbe1df58234ef1944a630f9f14925","observation_id":"527739e5-4c09-4e9f-9fad-92a672f9b7e4","resolution":{"observed_at":"2026-08-07T05:28:35.345952Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:35.400200Z","title":"Early and late-onset effect of chronic stress on vascular function in mice: a possible model of the impact of depression on vascular disease in aging,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:35.400200Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:e83276d6630d09e76154e5d6b9bbd8abb8a07c24d102aa379ff3aa9e8ae0edc7","observation_id":"8a0ccb3c-3760-4ba5-8dff-4e09847654b3","resolution":{"observed_at":"2026-08-07T05:28:35.400200Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:35.511077Z","title":"Stress, depression and parkinson’s disease,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:35.511077Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:6ceb9f1020acad26e21fe94588b39d2e978bdb5cccc152b24cfca8ceab2d3ab6","observation_id":"ad2e2f61-8d5d-465f-b51c-ca4f8b548e45","resolution":{"observed_at":"2026-08-07T05:28:35.511077Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:35.597880Z","title":"Stress, de- pression, the immune system, and cancer,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:35.597880Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:41ebec88d64314d600c8c7c944135c4a27618dd8b7db30b3921b016ddbe60414","observation_id":"ff0b9798-d56b-421a-a5a1-9151de5ac9f5","resolution":{"observed_at":"2026-08-07T05:28:35.597880Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:35.670502Z","title":"Biobehavioral, immune, and health benefits following recurrence for psycholog- ical intervention participants,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:35.670502Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:7a4f0a24a942770c0ababe6e715f49b3da0104caf9dcaf24583922979ccd90ad","observation_id":"f8d09247-99ae-4001-a489-b05f6c29a3f7","resolution":{"observed_at":"2026-08-07T05:28:35.670502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:35.773528Z","title":"Depression as a predictor of disease progression and mortality in cancer patients: a meta-analysis,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:35.773528Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:450eb44873c18d1fd6799226751ebfd535c0c1d778d6efff136ef963642ced32","observation_id":"def3495f-db65-4a63-bd42-f900d36e694c","resolution":{"observed_at":"2026-08-07T05:28:35.773528Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:35.892796Z","title":"Prevention of suicidal behavior,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:35.892796Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:c6b00399d36cb77de817d61d02df15cfd8bf7821fdf317acda0bf331966134d2","observation_id":"cab7cc36-df2e-4e87-89a3-3d64e96dde71","resolution":{"observed_at":"2026-08-07T05:28:35.892796Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:35.976547Z","title":"Prevalence of suicide attempt in individuals with major depressive disorder: a meta-analysis of observational surveys,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:35.976547Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:94d9eba416e61332a540fa1f76fc550558f528c29e949cce8569a677904b6c17","observation_id":"85dc5262-3ca7-4d65-b15d-a0f43c5b2de2","resolution":{"observed_at":"2026-08-07T05:28:35.976547Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:36.044727Z","title":"Predictors of suicidal ideation, suicide attempt and suicide death among people with major depressive disorder: A systematic review and meta-analysis of cohort studies,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:36.044727Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:0d1c8aba11ee29d5c99b50919a03db5cc757776f9a0ed1003389f992dc1739d1","observation_id":"18c5fb49-6652-4359-a265-20f8060e8756","resolution":{"observed_at":"2026-08-07T05:28:36.044727Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:36.119421Z","title":"Problematic interpersonal relation- ships at work and depression: a swedish prospective cohort study,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:36.119421Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:1fbf6c15e10175b837d9685c82bb1e8447b416030c79b56d8a788949402202bc","observation_id":"c5101fa5-37c8-478f-82bf-a2f1fcf7498b","resolution":{"observed_at":"2026-08-07T05:28:36.119421Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:36.208823Z","title":"The social cost of depression: Investigating the impact of impaired social emotion regulation, social cognition, and interpersonal behavior on social function- ing,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:36.208823Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:1bfbf965c48c768327340fe70d4a13cc3f2e3b9ca90732cd24021961d621e29f","observation_id":"0aff0f69-a3e6-4677-99ea-698edf9feae2","resolution":{"observed_at":"2026-08-07T05:28:36.208823Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:36.287111Z","title":"Depression- related psychosocial variables: Are they specific to depression in adolescents?","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:36.287111Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:126a1234e31089e1768f6b038bcf54767a2fca8e944a523b34563f3b040d6473","observation_id":"55cf2b51-faed-4cb0-897f-4fb188df99e9","resolution":{"observed_at":"2026-08-07T05:28:36.287111Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:36.360596Z","title":"Links between depression and substance abuse in adolescents: neurobiological mechanisms,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:36.360596Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:1b6c3ef59f58023404bacc7570ac8ec5bd54e43d5af05d5e4f0b3e7d1b8b2b76","observation_id":"75523a4b-88a9-42e1-bec8-9332acac70ac","resolution":{"observed_at":"2026-08-07T05:28:36.360596Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:36.440387Z","title":"Regional metabolic effects of fluoxetine in major depression: serial changes and relationship to clinical response,","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:36.440387Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:b50bae32d498dc1c49a1a71347d8515442469deb47fd6882147f5bc93356b723","observation_id":"6bf84801-a850-45e7-8e0e-ae6407fedbcb","resolution":{"observed_at":"2026-08-07T05:28:36.440387Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:36.546587Z","title":"Co-altered functional networks and brain structure in unmedicated patients with bipolar and major depressive disorders,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:36.546587Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:c3dd56a9def41fb22d22507ae2cf171f753dab49b2f05e9fdca1ef2fecc986f5","observation_id":"05266a6d-3dc9-4089-b22a-05179bfb4f3e","resolution":{"observed_at":"2026-08-07T05:28:36.546587Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:36.685821Z","title":"Eeg alpha asymmetry, depression, and cognitive functioning,","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:36.685821Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:3ede3e626bc8e844b330d5f1b27038dd94ee701223d5b95de17acbf5d6f967e6","observation_id":"43220a8f-b25a-44c9-abfc-c8092e0e1771","resolution":{"observed_at":"2026-08-07T05:28:36.685821Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:36.786382Z","title":"Neurophysiological correlates of depressive symp- toms in young adults: a quantitative eeg study,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:36.786382Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:8cdc953b5f1aed456dee09bde32359be4b49cc8c86163253bc54e285801d8a03","observation_id":"d18d25fa-e1fc-4ff2-9ffd-e818f4861294","resolution":{"observed_at":"2026-08-07T05:28:36.786382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:36.900117Z","title":"Altered brain dynamics and their ability for major depression detection using eeg microstates analysis,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:36.900117Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:4eaa8c2e882edc1196acb723a3da3894a88ba1cb8bca2c22ad603d5f4abeb888","observation_id":"f1a2e688-c16c-43b7-a6e0-ca6c6b867364","resolution":{"observed_at":"2026-08-07T05:28:36.900117Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:36.988660Z","title":"Sex differences in diencephalon serotonin transporter availability in major depression,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey","version":2},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:36.988660Z"},"links":{"citing_paper":"/paper/2506.18915"},"observation_digest":"sha256:6fcbf2b77e680a94ba194a599b27daf7341a287bcdb32ac9256de6c5c6fe4d4b","observation_id":"492cfb8f-a259-4610-b925-858c61305af0","resolution":{"observed_at":"2026-08-07T05:28:36.988660Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.18915","last_updated":"2025-06-29T10:44:28Z","latest_version":2,"primary_category":"q-bio.NC","snapshot_observed_at":"2026-08-12T22:26:39.096296Z","submitted_at":"2025-06-09T14:40:16Z","title":"Automatic Depression Assessment using Machine Learning: A Comprehensive Survey"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":100,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":300},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 100 of 300 outbound references and 0 inbound Pith citation observations for arXiv:2506.18915."}