{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:ZCJU7NPBGSESTQVPHMAXNAMTYO","short_pith_number":"pith:ZCJU7NPB","schema_version":"1.0","canonical_sha256":"c8934fb5e1348929c2af3b01768193c39a72516e2e5ae6846a777a36cbf2779d","source":{"kind":"arxiv","id":"2608.16273","version":1},"attestation_state":"computed","paper":{"title":"Foresight-England: Development of a National-Scale Generative AI Model of Electronic Health Records for Medical Event Prediction across the COVID-19 Pandemic","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Angela M. Wood, Anoop D. Shah, Cathie Sudlow, Christopher Tomlinson, Harry Hemingway, Richard Dobson, Simon Ellershaw, Spiros Denaxas, Zeljko Kraljevic","submitted_at":"2026-08-17T08:46:52Z","abstract_excerpt":"Foresight-England (Foresight-E) is the first national-scale generative foundation model of electronic health records (EHRs), developed as a research pilot strictly for COVID-19 research. We evaluated its ability to model the direct and indirect effects of the pandemic. Trained from scratch entirely within the NHS England Secure Data Environment, Foresight-E is a 243-million-parameter transformer decoder. It was trained and evaluated on de-identified, longitudinal EHRs of approximately 61 million individuals, integrating primary/secondary care, death registrations, and COVID-19 data. Training a"},"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":"2608.16273","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-08-17T08:46:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6d3b4090ac1d30a552ec2cf3a3d905c71493c8e019bf1b3edb4ca1fd722c57ae","abstract_canon_sha256":"14ae622977b761fe8f20fbfe963a8cd30f36f091ba4c54c33100d9e62e71fbac"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-18T02:17:39.108708Z","signature_b64":"U6NX6V8DWD8ygQe/1KLphwYWqKMxzC6HxZ2v4u/zibp41ldLcvbm+OZ+5nLj6iv83FshmbFztvsz85hsYpxdBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c8934fb5e1348929c2af3b01768193c39a72516e2e5ae6846a777a36cbf2779d","last_reissued_at":"2026-08-18T02:17:39.106934Z","signature_status":"signed_v1","first_computed_at":"2026-08-18T02:17:39.106934Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Foresight-England: Development of a National-Scale Generative AI Model of Electronic Health Records for Medical Event Prediction across the COVID-19 Pandemic","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Angela M. Wood, Anoop D. Shah, Cathie Sudlow, Christopher Tomlinson, Harry Hemingway, Richard Dobson, Simon Ellershaw, Spiros Denaxas, Zeljko Kraljevic","submitted_at":"2026-08-17T08:46:52Z","abstract_excerpt":"Foresight-England (Foresight-E) is the first national-scale generative foundation model of electronic health records (EHRs), developed as a research pilot strictly for COVID-19 research. We evaluated its ability to model the direct and indirect effects of the pandemic. Trained from scratch entirely within the NHS England Secure Data Environment, Foresight-E is a 243-million-parameter transformer decoder. It was trained and evaluated on de-identified, longitudinal EHRs of approximately 61 million individuals, integrating primary/secondary care, death registrations, and COVID-19 data. Training a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.16273","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/2608.16273/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":"2608.16273","created_at":"2026-08-18T02:17:39.106659+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.16273v1","created_at":"2026-08-18T02:17:39.106659+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.16273","created_at":"2026-08-18T02:17:39.106659+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZCJU7NPBGSES","created_at":"2026-08-18T02:17:39.106659+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZCJU7NPBGSESTQVP","created_at":"2026-08-18T02:17:39.106659+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZCJU7NPB","created_at":"2026-08-18T02:17:39.106659+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZCJU7NPBGSESTQVPHMAXNAMTYO","json":"https://pith.science/pith/ZCJU7NPBGSESTQVPHMAXNAMTYO.json","graph_json":"https://pith.science/api/pith-number/ZCJU7NPBGSESTQVPHMAXNAMTYO/graph.json","events_json":"https://pith.science/api/pith-number/ZCJU7NPBGSESTQVPHMAXNAMTYO/events.json","paper":"https://pith.science/paper/ZCJU7NPB"},"agent_actions":{"view_html":"https://pith.science/pith/ZCJU7NPBGSESTQVPHMAXNAMTYO","download_json":"https://pith.science/pith/ZCJU7NPBGSESTQVPHMAXNAMTYO.json","view_paper":"https://pith.science/paper/ZCJU7NPB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.16273&json=true","fetch_graph":"https://pith.science/api/pith-number/ZCJU7NPBGSESTQVPHMAXNAMTYO/graph.json","fetch_events":"https://pith.science/api/pith-number/ZCJU7NPBGSESTQVPHMAXNAMTYO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZCJU7NPBGSESTQVPHMAXNAMTYO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZCJU7NPBGSESTQVPHMAXNAMTYO/action/storage_attestation","attest_author":"https://pith.science/pith/ZCJU7NPBGSESTQVPHMAXNAMTYO/action/author_attestation","sign_citation":"https://pith.science/pith/ZCJU7NPBGSESTQVPHMAXNAMTYO/action/citation_signature","submit_replication":"https://pith.science/pith/ZCJU7NPBGSESTQVPHMAXNAMTYO/action/replication_record"}},"created_at":"2026-08-18T02:17:39.106659+00:00","updated_at":"2026-08-18T02:17:39.106659+00:00"}