{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:ECK3NAQKDYOA77T3ZQQFQFGMMK","short_pith_number":"pith:ECK3NAQK","canonical_record":{"source":{"id":"2206.13516","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-06-27T06:37:15Z","cross_cats_sorted":[],"title_canon_sha256":"2f71d362bedfaf7f6f73aa46f42dd4d6efb3bd9b5a385f3fae84ac4cc4dd74ab","abstract_canon_sha256":"adea2092ee5589fbba34c78f0860c933f70c68f1631fd072f5f5ee7c3e7a3bc0"},"schema_version":"1.0"},"canonical_sha256":"2095b6820a1e1c0ffe7bcc205814cc62a58891044ac32e76f9829f1b5327ecfb","source":{"kind":"arxiv","id":"2206.13516","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.13516","created_at":"2026-07-05T04:35:22Z"},{"alias_kind":"arxiv_version","alias_value":"2206.13516v1","created_at":"2026-07-05T04:35:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.13516","created_at":"2026-07-05T04:35:22Z"},{"alias_kind":"pith_short_12","alias_value":"ECK3NAQKDYOA","created_at":"2026-07-05T04:35:22Z"},{"alias_kind":"pith_short_16","alias_value":"ECK3NAQKDYOA77T3","created_at":"2026-07-05T04:35:22Z"},{"alias_kind":"pith_short_8","alias_value":"ECK3NAQK","created_at":"2026-07-05T04:35:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:ECK3NAQKDYOA77T3ZQQFQFGMMK","target":"record","payload":{"canonical_record":{"source":{"id":"2206.13516","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-06-27T06:37:15Z","cross_cats_sorted":[],"title_canon_sha256":"2f71d362bedfaf7f6f73aa46f42dd4d6efb3bd9b5a385f3fae84ac4cc4dd74ab","abstract_canon_sha256":"adea2092ee5589fbba34c78f0860c933f70c68f1631fd072f5f5ee7c3e7a3bc0"},"schema_version":"1.0"},"canonical_sha256":"2095b6820a1e1c0ffe7bcc205814cc62a58891044ac32e76f9829f1b5327ecfb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:35:22.596403Z","signature_b64":"kngacguNucPRK6OJP89vV7kF4vtKGi026otsi+D9gwgaU2p4VRtBY0FndV1GQsSB9GCcZ4FjoH3MSN9S6/SvAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2095b6820a1e1c0ffe7bcc205814cc62a58891044ac32e76f9829f1b5327ecfb","last_reissued_at":"2026-07-05T04:35:22.596007Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:35:22.596007Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2206.13516","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":"2026-07-05T04:35:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Jv3xB/6kugbhpupdz04tqnKkiFxxtX4aGtCjLA3BZtIgI6zJJa48SlLG6GctPqbObrxYYzFbzV39v5XT1iLLCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T08:32:00.890176Z"},"content_sha256":"34d09c4d22fb2336ce86aa0fcef3c62eeeddb8de0bd15a64078f523909366834","schema_version":"1.0","event_id":"sha256:34d09c4d22fb2336ce86aa0fcef3c62eeeddb8de0bd15a64078f523909366834"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:ECK3NAQKDYOA77T3ZQQFQFGMMK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improving Clinical Efficiency and Reducing Medical Errors through NLP-enabled diagnosis of Health Conditions from Transcription Reports","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Kabir Ramzan, Krish Maniar, Shafin Haque","submitted_at":"2022-06-27T06:37:15Z","abstract_excerpt":"Misdiagnosis rates are one of the leading causes of medical errors in hospitals, affecting over 12 million adults across the US. To address the high rate of misdiagnosis, this study utilizes 4 NLP-based algorithms to determine the appropriate health condition based on an unstructured transcription report. From the Logistic Regression, Random Forest, LSTM, and CNNLSTM models, the CNN-LSTM model performed the best with an accuracy of 97.89%. We packaged this model into a authenticated web platform for accessible assistance to clinicians. Overall, by standardizing health care diagnosis and struct"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.13516","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/2206.13516/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":"2026-07-05T04:35:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Eo+jCBuxCe24T1V4/wuBiO4u2JsNv1OWv/5aQBIkLLk5/RoZSYCucmqj/GKqyNwbG0BbmsLYQPA67X1CLd+KCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T08:32:00.890585Z"},"content_sha256":"c8e177ec597056ed09a38751520920fdae96a93663dd678585019ac9ea373bda","schema_version":"1.0","event_id":"sha256:c8e177ec597056ed09a38751520920fdae96a93663dd678585019ac9ea373bda"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ECK3NAQKDYOA77T3ZQQFQFGMMK/bundle.json","state_url":"https://pith.science/pith/ECK3NAQKDYOA77T3ZQQFQFGMMK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ECK3NAQKDYOA77T3ZQQFQFGMMK/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":"2026-07-06T08:32:00Z","links":{"resolver":"https://pith.science/pith/ECK3NAQKDYOA77T3ZQQFQFGMMK","bundle":"https://pith.science/pith/ECK3NAQKDYOA77T3ZQQFQFGMMK/bundle.json","state":"https://pith.science/pith/ECK3NAQKDYOA77T3ZQQFQFGMMK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ECK3NAQKDYOA77T3ZQQFQFGMMK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:ECK3NAQKDYOA77T3ZQQFQFGMMK","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":"adea2092ee5589fbba34c78f0860c933f70c68f1631fd072f5f5ee7c3e7a3bc0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-06-27T06:37:15Z","title_canon_sha256":"2f71d362bedfaf7f6f73aa46f42dd4d6efb3bd9b5a385f3fae84ac4cc4dd74ab"},"schema_version":"1.0","source":{"id":"2206.13516","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.13516","created_at":"2026-07-05T04:35:22Z"},{"alias_kind":"arxiv_version","alias_value":"2206.13516v1","created_at":"2026-07-05T04:35:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.13516","created_at":"2026-07-05T04:35:22Z"},{"alias_kind":"pith_short_12","alias_value":"ECK3NAQKDYOA","created_at":"2026-07-05T04:35:22Z"},{"alias_kind":"pith_short_16","alias_value":"ECK3NAQKDYOA77T3","created_at":"2026-07-05T04:35:22Z"},{"alias_kind":"pith_short_8","alias_value":"ECK3NAQK","created_at":"2026-07-05T04:35:22Z"}],"graph_snapshots":[{"event_id":"sha256:c8e177ec597056ed09a38751520920fdae96a93663dd678585019ac9ea373bda","target":"graph","created_at":"2026-07-05T04:35:22Z","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/2206.13516/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Misdiagnosis rates are one of the leading causes of medical errors in hospitals, affecting over 12 million adults across the US. To address the high rate of misdiagnosis, this study utilizes 4 NLP-based algorithms to determine the appropriate health condition based on an unstructured transcription report. From the Logistic Regression, Random Forest, LSTM, and CNNLSTM models, the CNN-LSTM model performed the best with an accuracy of 97.89%. We packaged this model into a authenticated web platform for accessible assistance to clinicians. Overall, by standardizing health care diagnosis and struct","authors_text":"Kabir Ramzan, Krish Maniar, Shafin Haque","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-06-27T06:37:15Z","title":"Improving Clinical Efficiency and Reducing Medical Errors through NLP-enabled diagnosis of Health Conditions from Transcription Reports"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.13516","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":"sha256:34d09c4d22fb2336ce86aa0fcef3c62eeeddb8de0bd15a64078f523909366834","target":"record","created_at":"2026-07-05T04:35:22Z","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":"adea2092ee5589fbba34c78f0860c933f70c68f1631fd072f5f5ee7c3e7a3bc0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-06-27T06:37:15Z","title_canon_sha256":"2f71d362bedfaf7f6f73aa46f42dd4d6efb3bd9b5a385f3fae84ac4cc4dd74ab"},"schema_version":"1.0","source":{"id":"2206.13516","kind":"arxiv","version":1}},"canonical_sha256":"2095b6820a1e1c0ffe7bcc205814cc62a58891044ac32e76f9829f1b5327ecfb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2095b6820a1e1c0ffe7bcc205814cc62a58891044ac32e76f9829f1b5327ecfb","first_computed_at":"2026-07-05T04:35:22.596007Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:35:22.596007Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kngacguNucPRK6OJP89vV7kF4vtKGi026otsi+D9gwgaU2p4VRtBY0FndV1GQsSB9GCcZ4FjoH3MSN9S6/SvAw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:35:22.596403Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.13516","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:34d09c4d22fb2336ce86aa0fcef3c62eeeddb8de0bd15a64078f523909366834","sha256:c8e177ec597056ed09a38751520920fdae96a93663dd678585019ac9ea373bda"],"state_sha256":"fcc4c97160215c8fba92f6133b6ca6e872c0f6ad59b86a0dfc7b7a76201b0945"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ctCHsVZGqdhWqkKtJEI+LPWBes3KOEG9B6Jyouoz5ztEC5iofPWDWqMH6gJesDX6Rvd4aexI94xtYFiVYbPsDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T08:32:00.892515Z","bundle_sha256":"ee9e6f523d1bbd3b122b7b38a4ee280886b1d1384b68148c72a283a5ec57d7e1"}}