{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:II5UJ7C577LG7N2SJ6J6EY5TRP","short_pith_number":"pith:II5UJ7C5","schema_version":"1.0","canonical_sha256":"423b44fc5dffd66fb7524f93e263b38be8fb81af64fabbdd6a5e38893794d837","source":{"kind":"arxiv","id":"1511.05942","version":11},"attestation_state":"computed","paper":{"title":"Doctor AI: Predicting Clinical Events via Recurrent Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Andy Schuetz, Edward Choi, Jimeng Sun, Mohammad Taha Bahadori, Walter F. Stewart","submitted_at":"2015-11-18T20:47:44Z","abstract_excerpt":"Leveraging large historical data in electronic health record (EHR), we developed Doctor AI, a generic predictive model that covers observed medical conditions and medication uses. Doctor AI is a temporal model using recurrent neural networks (RNN) and was developed and applied to longitudinal time stamped EHR data from 260K patients over 8 years. Encounter records (e.g. diagnosis codes, medication codes or procedure codes) were input to RNN to predict (all) the diagnosis and medication categories for a subsequent visit. Doctor AI assesses the history of patients to make multilabel predictions "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1511.05942","kind":"arxiv","version":11},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-11-18T20:47:44Z","cross_cats_sorted":[],"title_canon_sha256":"4e8a365c33ce7e205a4a1c97a087d7c6e554867b780a2f41a00aff0426f39805","abstract_canon_sha256":"5c91afd85b1b57e081c986e3f3bbdc53a2f21cdff23c8ffd4442df9df4e034f1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:03:44.274212Z","signature_b64":"YYF0Dv6Qdy5DJdB7KqsSNgjgD1JpfipWTgd6PQ5TJ/5tEB0+9JKAJMvY1cm6C9RyOD5utt9QAnDjiz+xM2B4DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"423b44fc5dffd66fb7524f93e263b38be8fb81af64fabbdd6a5e38893794d837","last_reissued_at":"2026-05-18T01:03:44.273668Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:03:44.273668Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Doctor AI: Predicting Clinical Events via Recurrent Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Andy Schuetz, Edward Choi, Jimeng Sun, Mohammad Taha Bahadori, Walter F. Stewart","submitted_at":"2015-11-18T20:47:44Z","abstract_excerpt":"Leveraging large historical data in electronic health record (EHR), we developed Doctor AI, a generic predictive model that covers observed medical conditions and medication uses. Doctor AI is a temporal model using recurrent neural networks (RNN) and was developed and applied to longitudinal time stamped EHR data from 260K patients over 8 years. Encounter records (e.g. diagnosis codes, medication codes or procedure codes) were input to RNN to predict (all) the diagnosis and medication categories for a subsequent visit. Doctor AI assesses the history of patients to make multilabel predictions "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.05942","kind":"arxiv","version":11},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1511.05942","created_at":"2026-05-18T01:03:44.273756+00:00"},{"alias_kind":"arxiv_version","alias_value":"1511.05942v11","created_at":"2026-05-18T01:03:44.273756+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.05942","created_at":"2026-05-18T01:03:44.273756+00:00"},{"alias_kind":"pith_short_12","alias_value":"II5UJ7C577LG","created_at":"2026-05-18T12:29:25.134429+00:00"},{"alias_kind":"pith_short_16","alias_value":"II5UJ7C577LG7N2S","created_at":"2026-05-18T12:29:25.134429+00:00"},{"alias_kind":"pith_short_8","alias_value":"II5UJ7C5","created_at":"2026-05-18T12:29:25.134429+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.16775","citing_title":"Representation Before Training: A Fixed-Budget Benchmark for Generative Medical Event Models","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/II5UJ7C577LG7N2SJ6J6EY5TRP","json":"https://pith.science/pith/II5UJ7C577LG7N2SJ6J6EY5TRP.json","graph_json":"https://pith.science/api/pith-number/II5UJ7C577LG7N2SJ6J6EY5TRP/graph.json","events_json":"https://pith.science/api/pith-number/II5UJ7C577LG7N2SJ6J6EY5TRP/events.json","paper":"https://pith.science/paper/II5UJ7C5"},"agent_actions":{"view_html":"https://pith.science/pith/II5UJ7C577LG7N2SJ6J6EY5TRP","download_json":"https://pith.science/pith/II5UJ7C577LG7N2SJ6J6EY5TRP.json","view_paper":"https://pith.science/paper/II5UJ7C5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1511.05942&json=true","fetch_graph":"https://pith.science/api/pith-number/II5UJ7C577LG7N2SJ6J6EY5TRP/graph.json","fetch_events":"https://pith.science/api/pith-number/II5UJ7C577LG7N2SJ6J6EY5TRP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/II5UJ7C577LG7N2SJ6J6EY5TRP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/II5UJ7C577LG7N2SJ6J6EY5TRP/action/storage_attestation","attest_author":"https://pith.science/pith/II5UJ7C577LG7N2SJ6J6EY5TRP/action/author_attestation","sign_citation":"https://pith.science/pith/II5UJ7C577LG7N2SJ6J6EY5TRP/action/citation_signature","submit_replication":"https://pith.science/pith/II5UJ7C577LG7N2SJ6J6EY5TRP/action/replication_record"}},"created_at":"2026-05-18T01:03:44.273756+00:00","updated_at":"2026-05-18T01:03:44.273756+00:00"}