{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:CKCKGU7NSPFJEMO6OHGW7BHK62","short_pith_number":"pith:CKCKGU7N","canonical_record":{"source":{"id":"2507.12774","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-17T04:31:55Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"47e1d2ac76dbacf867af7f227e2f83a03e5f1efbe81574a64c4d9f64c79d2531","abstract_canon_sha256":"36aca89519c429ddc01095c65df7da6c73ce46f26a2c9eebe47fcdb646495d8d"},"schema_version":"1.0"},"canonical_sha256":"1284a353ed93ca9231de71cd6f84eaf6a47565616fb4ea28050b6c9e6416b5c4","source":{"kind":"arxiv","id":"2507.12774","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.12774","created_at":"2026-07-05T11:38:38Z"},{"alias_kind":"arxiv_version","alias_value":"2507.12774v1","created_at":"2026-07-05T11:38:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.12774","created_at":"2026-07-05T11:38:38Z"},{"alias_kind":"pith_short_12","alias_value":"CKCKGU7NSPFJ","created_at":"2026-07-05T11:38:38Z"},{"alias_kind":"pith_short_16","alias_value":"CKCKGU7NSPFJEMO6","created_at":"2026-07-05T11:38:38Z"},{"alias_kind":"pith_short_8","alias_value":"CKCKGU7N","created_at":"2026-07-05T11:38:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:CKCKGU7NSPFJEMO6OHGW7BHK62","target":"record","payload":{"canonical_record":{"source":{"id":"2507.12774","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-17T04:31:55Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"47e1d2ac76dbacf867af7f227e2f83a03e5f1efbe81574a64c4d9f64c79d2531","abstract_canon_sha256":"36aca89519c429ddc01095c65df7da6c73ce46f26a2c9eebe47fcdb646495d8d"},"schema_version":"1.0"},"canonical_sha256":"1284a353ed93ca9231de71cd6f84eaf6a47565616fb4ea28050b6c9e6416b5c4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:38:38.893085Z","signature_b64":"uSdpLiQyzmT/b5nbTRB/TNBPiqpS+stQNEzN9pcjPv9GrDmzFWnKgeH5b3ocFQmN3xycMFmZ0H5b30vHHge9Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1284a353ed93ca9231de71cd6f84eaf6a47565616fb4ea28050b6c9e6416b5c4","last_reissued_at":"2026-07-05T11:38:38.892662Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:38:38.892662Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.12774","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":"2026-07-05T11:38:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z8ZzXk5+c/IemIRPHmzr5UD4UHO1s9rmI5+168ZkTvjZabq4s2sEQkmp9sNNerTFAt3UNQStT+DALaFrm7CTBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T10:24:57.610777Z"},"content_sha256":"b2db240f6a5061c143ee64dc4265fe3e4f212e97fe76ef893c00758264d04e73","schema_version":"1.0","event_id":"sha256:b2db240f6a5061c143ee64dc4265fe3e4f212e97fe76ef893c00758264d04e73"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:CKCKGU7NSPFJEMO6OHGW7BHK62","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Comprehensive Survey of Electronic Health Record Modeling: From Deep Learning Approaches to Large Language Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Jingxi Zhu, Tianxiang Zhao, Vasant Honavar, Weijieying Ren, Zehao Liu","submitted_at":"2025-07-17T04:31:55Z","abstract_excerpt":"Artificial intelligence (AI) has demonstrated significant potential in transforming healthcare through the analysis and modeling of electronic health records (EHRs). However, the inherent heterogeneity, temporal irregularity, and domain-specific nature of EHR data present unique challenges that differ fundamentally from those in vision and natural language tasks. This survey offers a comprehensive overview of recent advancements at the intersection of deep learning, large language models (LLMs), and EHR modeling. We introduce a unified taxonomy that spans five key design dimensions: data-centr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.12774","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/2507.12774/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":"2026-07-05T11:38:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qnf0BxouVdNutIVerh0BGA9V26P6OU2H/Y/7Ywbge6B6+1BN7uLggGTYQj3Sy69eOXNVje5tahlUhr7ghYNpDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T10:24:57.611386Z"},"content_sha256":"04dec0c5fbb7285ae9729d0d83d0b89c97a96e1d5c08b7dc1a1fdfe4393ce050","schema_version":"1.0","event_id":"sha256:04dec0c5fbb7285ae9729d0d83d0b89c97a96e1d5c08b7dc1a1fdfe4393ce050"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CKCKGU7NSPFJEMO6OHGW7BHK62/bundle.json","state_url":"https://pith.science/pith/CKCKGU7NSPFJEMO6OHGW7BHK62/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CKCKGU7NSPFJEMO6OHGW7BHK62/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":"2026-08-15T10:24:57Z","links":{"resolver":"https://pith.science/pith/CKCKGU7NSPFJEMO6OHGW7BHK62","bundle":"https://pith.science/pith/CKCKGU7NSPFJEMO6OHGW7BHK62/bundle.json","state":"https://pith.science/pith/CKCKGU7NSPFJEMO6OHGW7BHK62/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CKCKGU7NSPFJEMO6OHGW7BHK62/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:CKCKGU7NSPFJEMO6OHGW7BHK62","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":"36aca89519c429ddc01095c65df7da6c73ce46f26a2c9eebe47fcdb646495d8d","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-17T04:31:55Z","title_canon_sha256":"47e1d2ac76dbacf867af7f227e2f83a03e5f1efbe81574a64c4d9f64c79d2531"},"schema_version":"1.0","source":{"id":"2507.12774","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.12774","created_at":"2026-07-05T11:38:38Z"},{"alias_kind":"arxiv_version","alias_value":"2507.12774v1","created_at":"2026-07-05T11:38:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.12774","created_at":"2026-07-05T11:38:38Z"},{"alias_kind":"pith_short_12","alias_value":"CKCKGU7NSPFJ","created_at":"2026-07-05T11:38:38Z"},{"alias_kind":"pith_short_16","alias_value":"CKCKGU7NSPFJEMO6","created_at":"2026-07-05T11:38:38Z"},{"alias_kind":"pith_short_8","alias_value":"CKCKGU7N","created_at":"2026-07-05T11:38:38Z"}],"graph_snapshots":[{"event_id":"sha256:04dec0c5fbb7285ae9729d0d83d0b89c97a96e1d5c08b7dc1a1fdfe4393ce050","target":"graph","created_at":"2026-07-05T11:38:38Z","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/2507.12774/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Artificial intelligence (AI) has demonstrated significant potential in transforming healthcare through the analysis and modeling of electronic health records (EHRs). However, the inherent heterogeneity, temporal irregularity, and domain-specific nature of EHR data present unique challenges that differ fundamentally from those in vision and natural language tasks. This survey offers a comprehensive overview of recent advancements at the intersection of deep learning, large language models (LLMs), and EHR modeling. We introduce a unified taxonomy that spans five key design dimensions: data-centr","authors_text":"Jingxi Zhu, Tianxiang Zhao, Vasant Honavar, Weijieying Ren, Zehao Liu","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-17T04:31:55Z","title":"A Comprehensive Survey of Electronic Health Record Modeling: From Deep Learning Approaches to Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.12774","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:b2db240f6a5061c143ee64dc4265fe3e4f212e97fe76ef893c00758264d04e73","target":"record","created_at":"2026-07-05T11:38:38Z","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":"36aca89519c429ddc01095c65df7da6c73ce46f26a2c9eebe47fcdb646495d8d","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-17T04:31:55Z","title_canon_sha256":"47e1d2ac76dbacf867af7f227e2f83a03e5f1efbe81574a64c4d9f64c79d2531"},"schema_version":"1.0","source":{"id":"2507.12774","kind":"arxiv","version":1}},"canonical_sha256":"1284a353ed93ca9231de71cd6f84eaf6a47565616fb4ea28050b6c9e6416b5c4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1284a353ed93ca9231de71cd6f84eaf6a47565616fb4ea28050b6c9e6416b5c4","first_computed_at":"2026-07-05T11:38:38.892662Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:38:38.892662Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uSdpLiQyzmT/b5nbTRB/TNBPiqpS+stQNEzN9pcjPv9GrDmzFWnKgeH5b3ocFQmN3xycMFmZ0H5b30vHHge9Bg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:38:38.893085Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.12774","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b2db240f6a5061c143ee64dc4265fe3e4f212e97fe76ef893c00758264d04e73","sha256:04dec0c5fbb7285ae9729d0d83d0b89c97a96e1d5c08b7dc1a1fdfe4393ce050"],"state_sha256":"954edd8ac03da63908161629cde978890408fb97c5dc400420ca956817aa6a34"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"D9v5j9fgst/XlEazh5tUbBdlda0zv1EaIqqj5giRxRBJp8ncNyKCRpvQutBwamPxcdgyTOyGyq2OrDegdxDbCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T10:24:57.616893Z","bundle_sha256":"a6e0314f6448cc975baabd971d551f0aa4e17aee5b92651b5dc2858a5164df6a"}}