{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:IW6IVO2CZSSRL33ZDFDDDEJI65","short_pith_number":"pith:IW6IVO2C","schema_version":"1.0","canonical_sha256":"45bc8abb42cca515ef791946319128f769913c444e55df8b01c21101af93bfe3","source":{"kind":"arxiv","id":"2409.13000","version":2},"attestation_state":"computed","paper":{"title":"Introducing the Large Medical Model: State of the art healthcare cost and risk prediction with transformers trained on patient event sequences","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","stat.AP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Asim Javed, David Wagner, Eric Marriott, Ethan Siegel, Flore Uzan, Ricky Sahu, Troy Yang","submitted_at":"2024-09-19T15:38:21Z","abstract_excerpt":"With U.S. healthcare spending approaching $5T (NHE Fact Sheet 2024), and 25% of it estimated to be wasteful (Waste in the US the health care system: estimated costs and potential for savings, n.d.), the need to better predict risk and optimal patient care is evermore important. This paper introduces the Large Medical Model (LMM), a generative pre-trained transformer (GPT) designed to guide and predict the broad facets of patient care and healthcare administration. The model is trained on medical event sequences from over 140M longitudinal patient claims records with a specialized vocabulary bu"},"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":"2409.13000","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-19T15:38:21Z","cross_cats_sorted":["cs.AI","stat.AP","stat.ML"],"title_canon_sha256":"74bf92d1cb9d7e986cebc00e7a9176a7c66433a54742fb8775a4d672bd80bddd","abstract_canon_sha256":"a1de9633ed4bb63d9b834b6a0ea440c0745eec676f990958548ac75b06b6d71c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:44:53.398598Z","signature_b64":"w/2c1K1O2zZYqlCC3GvY7SNAmbZftS7D5tamnNZif4tIAArAV83xmUR4IIyaniOBXZNfe6KRlpPM0Py1brbADw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45bc8abb42cca515ef791946319128f769913c444e55df8b01c21101af93bfe3","last_reissued_at":"2026-07-05T09:44:53.398017Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:44:53.398017Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Introducing the Large Medical Model: State of the art healthcare cost and risk prediction with transformers trained on patient event sequences","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","stat.AP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Asim Javed, David Wagner, Eric Marriott, Ethan Siegel, Flore Uzan, Ricky Sahu, Troy Yang","submitted_at":"2024-09-19T15:38:21Z","abstract_excerpt":"With U.S. healthcare spending approaching $5T (NHE Fact Sheet 2024), and 25% of it estimated to be wasteful (Waste in the US the health care system: estimated costs and potential for savings, n.d.), the need to better predict risk and optimal patient care is evermore important. This paper introduces the Large Medical Model (LMM), a generative pre-trained transformer (GPT) designed to guide and predict the broad facets of patient care and healthcare administration. The model is trained on medical event sequences from over 140M longitudinal patient claims records with a specialized vocabulary bu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.13000","kind":"arxiv","version":2},"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/2409.13000/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2409.13000","created_at":"2026-07-05T09:44:53.398080+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.13000v2","created_at":"2026-07-05T09:44:53.398080+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.13000","created_at":"2026-07-05T09:44:53.398080+00:00"},{"alias_kind":"pith_short_12","alias_value":"IW6IVO2CZSSR","created_at":"2026-07-05T09:44:53.398080+00:00"},{"alias_kind":"pith_short_16","alias_value":"IW6IVO2CZSSRL33Z","created_at":"2026-07-05T09:44:53.398080+00:00"},{"alias_kind":"pith_short_8","alias_value":"IW6IVO2C","created_at":"2026-07-05T09:44:53.398080+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.02740","citing_title":"Foundation Models to Unlock Real-World Evidence from Nationwide Medical Claims","ref_index":21,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IW6IVO2CZSSRL33ZDFDDDEJI65","json":"https://pith.science/pith/IW6IVO2CZSSRL33ZDFDDDEJI65.json","graph_json":"https://pith.science/api/pith-number/IW6IVO2CZSSRL33ZDFDDDEJI65/graph.json","events_json":"https://pith.science/api/pith-number/IW6IVO2CZSSRL33ZDFDDDEJI65/events.json","paper":"https://pith.science/paper/IW6IVO2C"},"agent_actions":{"view_html":"https://pith.science/pith/IW6IVO2CZSSRL33ZDFDDDEJI65","download_json":"https://pith.science/pith/IW6IVO2CZSSRL33ZDFDDDEJI65.json","view_paper":"https://pith.science/paper/IW6IVO2C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.13000&json=true","fetch_graph":"https://pith.science/api/pith-number/IW6IVO2CZSSRL33ZDFDDDEJI65/graph.json","fetch_events":"https://pith.science/api/pith-number/IW6IVO2CZSSRL33ZDFDDDEJI65/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IW6IVO2CZSSRL33ZDFDDDEJI65/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IW6IVO2CZSSRL33ZDFDDDEJI65/action/storage_attestation","attest_author":"https://pith.science/pith/IW6IVO2CZSSRL33ZDFDDDEJI65/action/author_attestation","sign_citation":"https://pith.science/pith/IW6IVO2CZSSRL33ZDFDDDEJI65/action/citation_signature","submit_replication":"https://pith.science/pith/IW6IVO2CZSSRL33ZDFDDDEJI65/action/replication_record"}},"created_at":"2026-07-05T09:44:53.398080+00:00","updated_at":"2026-07-05T09:44:53.398080+00:00"}