{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:GV5P4BLW3BUOFROBTE67V5NHL5","short_pith_number":"pith:GV5P4BLW","canonical_record":{"source":{"id":"2107.02975","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-07-07T01:50:02Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ef1d6f41ec9423c8a55edb79f39dde707013a50e79ae80abb03971d858754b9e","abstract_canon_sha256":"d4e8b82e0f1029582bd123f034c2801e6c039a6a4ff2a8502ed1f218676801c1"},"schema_version":"1.0"},"canonical_sha256":"357afe0576d868e2c5c1993dfaf5a75f6b32c1b72ff551d26eee024c93fc6868","source":{"kind":"arxiv","id":"2107.02975","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.02975","created_at":"2026-07-05T02:55:58Z"},{"alias_kind":"arxiv_version","alias_value":"2107.02975v1","created_at":"2026-07-05T02:55:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.02975","created_at":"2026-07-05T02:55:58Z"},{"alias_kind":"pith_short_12","alias_value":"GV5P4BLW3BUO","created_at":"2026-07-05T02:55:58Z"},{"alias_kind":"pith_short_16","alias_value":"GV5P4BLW3BUOFROB","created_at":"2026-07-05T02:55:58Z"},{"alias_kind":"pith_short_8","alias_value":"GV5P4BLW","created_at":"2026-07-05T02:55:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:GV5P4BLW3BUOFROBTE67V5NHL5","target":"record","payload":{"canonical_record":{"source":{"id":"2107.02975","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-07-07T01:50:02Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ef1d6f41ec9423c8a55edb79f39dde707013a50e79ae80abb03971d858754b9e","abstract_canon_sha256":"d4e8b82e0f1029582bd123f034c2801e6c039a6a4ff2a8502ed1f218676801c1"},"schema_version":"1.0"},"canonical_sha256":"357afe0576d868e2c5c1993dfaf5a75f6b32c1b72ff551d26eee024c93fc6868","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:55:58.817999Z","signature_b64":"GFcV739dB5Z4p6MpSh7FVOu8tEiWwqwmG3jGk3j59ked+Cjj/7JJwYBop0cDJ/zKbdHxcot0wOKuvzRGnQfTDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"357afe0576d868e2c5c1993dfaf5a75f6b32c1b72ff551d26eee024c93fc6868","last_reissued_at":"2026-07-05T02:55:58.817484Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:55:58.817484Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2107.02975","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:55:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VggdxzphoMspco9kmodJKk/ldKG0LHUnstXP1F6856221qEuysfbIUGfEr6WreYpViPmLaBnrQ2AtEDy7gMEBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T06:33:59.652118Z"},"content_sha256":"bcf43b8b0998c64c717e77b796b1d5fe1aae554293b49e27c7f614b68944b76a","schema_version":"1.0","event_id":"sha256:bcf43b8b0998c64c717e77b796b1d5fe1aae554293b49e27c7f614b68944b76a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:GV5P4BLW3BUOFROBTE67V5NHL5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Benjamin Rosand, David Chang, Dragomir Radev, Harlan M. Krumholz, Irene Li, Jeremy Goldwasser, Jessica Pan, Matthew Zhang, Muhammed Yavuz Nuzumlal{\\i}, Neha Verma, R. Andrew Taylor, Wai Pan Wong, Yixin Li","submitted_at":"2021-07-07T01:50:02Z","abstract_excerpt":"Electronic health records (EHRs), digital collections of patient healthcare events and observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and research. Despite this central role, EHRs are notoriously difficult to process automatically. Well over half of the information stored within EHRs is in the form of unstructured text (e.g. provider notes, operation reports) and remains largely untapped for secondary use. Recently, however, newer neural network and deep learning approaches to Natural Language Processing (NLP) have made considerable advances, outperfo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.02975","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2107.02975/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:55:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4gocKXzt2kAJhaTRbO2DokRVyfgClFT9NyBzev3KhPWf2l4Fu5lFqi3bS4Z1k9km0xUGZD5Hwxor7PtRnj2oCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T06:33:59.652453Z"},"content_sha256":"17162fe152d5e20af064b00853a85db35bd9bc9c23abf6487bc0461441563623","schema_version":"1.0","event_id":"sha256:17162fe152d5e20af064b00853a85db35bd9bc9c23abf6487bc0461441563623"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GV5P4BLW3BUOFROBTE67V5NHL5/bundle.json","state_url":"https://pith.science/pith/GV5P4BLW3BUOFROBTE67V5NHL5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GV5P4BLW3BUOFROBTE67V5NHL5/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-23T06:33:59Z","links":{"resolver":"https://pith.science/pith/GV5P4BLW3BUOFROBTE67V5NHL5","bundle":"https://pith.science/pith/GV5P4BLW3BUOFROBTE67V5NHL5/bundle.json","state":"https://pith.science/pith/GV5P4BLW3BUOFROBTE67V5NHL5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GV5P4BLW3BUOFROBTE67V5NHL5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:GV5P4BLW3BUOFROBTE67V5NHL5","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d4e8b82e0f1029582bd123f034c2801e6c039a6a4ff2a8502ed1f218676801c1","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-07-07T01:50:02Z","title_canon_sha256":"ef1d6f41ec9423c8a55edb79f39dde707013a50e79ae80abb03971d858754b9e"},"schema_version":"1.0","source":{"id":"2107.02975","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.02975","created_at":"2026-07-05T02:55:58Z"},{"alias_kind":"arxiv_version","alias_value":"2107.02975v1","created_at":"2026-07-05T02:55:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.02975","created_at":"2026-07-05T02:55:58Z"},{"alias_kind":"pith_short_12","alias_value":"GV5P4BLW3BUO","created_at":"2026-07-05T02:55:58Z"},{"alias_kind":"pith_short_16","alias_value":"GV5P4BLW3BUOFROB","created_at":"2026-07-05T02:55:58Z"},{"alias_kind":"pith_short_8","alias_value":"GV5P4BLW","created_at":"2026-07-05T02:55:58Z"}],"graph_snapshots":[{"event_id":"sha256:17162fe152d5e20af064b00853a85db35bd9bc9c23abf6487bc0461441563623","target":"graph","created_at":"2026-07-05T02:55:58Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2107.02975/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Electronic health records (EHRs), digital collections of patient healthcare events and observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and research. Despite this central role, EHRs are notoriously difficult to process automatically. Well over half of the information stored within EHRs is in the form of unstructured text (e.g. provider notes, operation reports) and remains largely untapped for secondary use. Recently, however, newer neural network and deep learning approaches to Natural Language Processing (NLP) have made considerable advances, outperfo","authors_text":"Benjamin Rosand, David Chang, Dragomir Radev, Harlan M. Krumholz, Irene Li, Jeremy Goldwasser, Jessica Pan, Matthew Zhang, Muhammed Yavuz Nuzumlal{\\i}, Neha Verma, R. Andrew Taylor, Wai Pan Wong, Yixin Li","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-07-07T01:50:02Z","title":"Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.02975","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:bcf43b8b0998c64c717e77b796b1d5fe1aae554293b49e27c7f614b68944b76a","target":"record","created_at":"2026-07-05T02:55:58Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d4e8b82e0f1029582bd123f034c2801e6c039a6a4ff2a8502ed1f218676801c1","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-07-07T01:50:02Z","title_canon_sha256":"ef1d6f41ec9423c8a55edb79f39dde707013a50e79ae80abb03971d858754b9e"},"schema_version":"1.0","source":{"id":"2107.02975","kind":"arxiv","version":1}},"canonical_sha256":"357afe0576d868e2c5c1993dfaf5a75f6b32c1b72ff551d26eee024c93fc6868","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"357afe0576d868e2c5c1993dfaf5a75f6b32c1b72ff551d26eee024c93fc6868","first_computed_at":"2026-07-05T02:55:58.817484Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:55:58.817484Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GFcV739dB5Z4p6MpSh7FVOu8tEiWwqwmG3jGk3j59ked+Cjj/7JJwYBop0cDJ/zKbdHxcot0wOKuvzRGnQfTDg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:55:58.817999Z","signed_message":"canonical_sha256_bytes"},"source_id":"2107.02975","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bcf43b8b0998c64c717e77b796b1d5fe1aae554293b49e27c7f614b68944b76a","sha256:17162fe152d5e20af064b00853a85db35bd9bc9c23abf6487bc0461441563623"],"state_sha256":"7e05c3211406a6468512e6781426989afe8c5615d34ca7273ce65c6b9291f211"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jSsb4k+cT8GxPXjRnRFpytZ469OR4Xkja/nLg0hlqtWdVDGNDiYqzL/YDwI9R5EQ2umU0pOfuyIbXwdjSVU2BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T06:33:59.656467Z","bundle_sha256":"1f0b112df8fc73e2e12ed2bc60da6dd7d025d629a0fed954dee991271ef04f0b"}}