{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:FDTDC3MCVDFL3PEY5A6DMYOS3O","short_pith_number":"pith:FDTDC3MC","canonical_record":{"source":{"id":"2410.02551","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-03T14:55:22Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"d31b5e5828a73badc2c3c3fbaba4c75a9b3f395c70545bcf715f5198c895255f","abstract_canon_sha256":"1a1cbd22ac82d579c20e121546c453910946d038de75db557e4c8c7bb428069a"},"schema_version":"1.0"},"canonical_sha256":"28e6316d82a8cabdbc98e83c3661d2dba3bc5fe402a4ba92c29c9ea25f69c72f","source":{"kind":"arxiv","id":"2410.02551","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.02551","created_at":"2026-07-05T10:20:15Z"},{"alias_kind":"arxiv_version","alias_value":"2410.02551v2","created_at":"2026-07-05T10:20:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.02551","created_at":"2026-07-05T10:20:15Z"},{"alias_kind":"pith_short_12","alias_value":"FDTDC3MCVDFL","created_at":"2026-07-05T10:20:15Z"},{"alias_kind":"pith_short_16","alias_value":"FDTDC3MCVDFL3PEY","created_at":"2026-07-05T10:20:15Z"},{"alias_kind":"pith_short_8","alias_value":"FDTDC3MC","created_at":"2026-07-05T10:20:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:FDTDC3MCVDFL3PEY5A6DMYOS3O","target":"record","payload":{"canonical_record":{"source":{"id":"2410.02551","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-03T14:55:22Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"d31b5e5828a73badc2c3c3fbaba4c75a9b3f395c70545bcf715f5198c895255f","abstract_canon_sha256":"1a1cbd22ac82d579c20e121546c453910946d038de75db557e4c8c7bb428069a"},"schema_version":"1.0"},"canonical_sha256":"28e6316d82a8cabdbc98e83c3661d2dba3bc5fe402a4ba92c29c9ea25f69c72f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:20:15.778341Z","signature_b64":"6cft6aNeVfLdccpYlBZ46N9xGZfCPmxlEfBSbPQai975D+Brbk/7JCKaAM6ahI0fOvXJW+TcLhCNwUgd/huXDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"28e6316d82a8cabdbc98e83c3661d2dba3bc5fe402a4ba92c29c9ea25f69c72f","last_reissued_at":"2026-07-05T10:20:15.777696Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:20:15.777696Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.02551","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:20:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CfrITcDhQ8yr3jtwkQpDcfI2WuAc/YiilmoV3Bwn97qmkSfQpr9VvAF3xfsTdZCviuHP+tqUTK6pdqwzEclzBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T23:28:41.237343Z"},"content_sha256":"cc986fce21f3c96234234b7ecc17deb3613b39b103303d57c9fb29254079c720","schema_version":"1.0","event_id":"sha256:cc986fce21f3c96234234b7ecc17deb3613b39b103303d57c9fb29254079c720"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:FDTDC3MCVDFL3PEY5A6DMYOS3O","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ColaCare: Enhancing Electronic Health Record Modeling through Large Language Model-Driven Multi-Agent Collaboration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Chengwei Pan, Dehao Sui, Ewen Harrison, Huiya Zhao, Junyi Gao, Liantao Ma, Tianlong Wang, Wen Tang, Xiaochen Zheng, Yasha Wang, Yinghao Zhu, Zixiang Wang","submitted_at":"2024-10-03T14:55:22Z","abstract_excerpt":"We introduce ColaCare, a framework that enhances Electronic Health Record (EHR) modeling through multi-agent collaboration driven by Large Language Models (LLMs). Our approach seamlessly integrates domain-specific expert models with LLMs to bridge the gap between structured EHR data and text-based reasoning. Inspired by the Multidisciplinary Team (MDT) approach used in clinical settings, ColaCare employs two types of agents: DoctorAgents and a MetaAgent, which collaboratively analyze patient data. Expert models process and generate predictions from numerical EHR data, while LLM agents produce "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.02551","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/2410.02551/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":"2026-07-05T10:20:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"slcMqdZOsl+Fy5qMx0WVm3dql+0oMmnByujq8Dnz1+DfMW6emctZ6fBBrvQMsdKR5MjBkn1dfY4m8kAlH+ZQCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T23:28:41.237849Z"},"content_sha256":"b54818bf4996b2b672c4c156cec8e0ae63f6fba1ad08a78fc8d171940a870f7b","schema_version":"1.0","event_id":"sha256:b54818bf4996b2b672c4c156cec8e0ae63f6fba1ad08a78fc8d171940a870f7b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FDTDC3MCVDFL3PEY5A6DMYOS3O/bundle.json","state_url":"https://pith.science/pith/FDTDC3MCVDFL3PEY5A6DMYOS3O/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FDTDC3MCVDFL3PEY5A6DMYOS3O/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":"2026-08-21T23:28:41Z","links":{"resolver":"https://pith.science/pith/FDTDC3MCVDFL3PEY5A6DMYOS3O","bundle":"https://pith.science/pith/FDTDC3MCVDFL3PEY5A6DMYOS3O/bundle.json","state":"https://pith.science/pith/FDTDC3MCVDFL3PEY5A6DMYOS3O/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FDTDC3MCVDFL3PEY5A6DMYOS3O/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:FDTDC3MCVDFL3PEY5A6DMYOS3O","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":"1a1cbd22ac82d579c20e121546c453910946d038de75db557e4c8c7bb428069a","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-03T14:55:22Z","title_canon_sha256":"d31b5e5828a73badc2c3c3fbaba4c75a9b3f395c70545bcf715f5198c895255f"},"schema_version":"1.0","source":{"id":"2410.02551","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.02551","created_at":"2026-07-05T10:20:15Z"},{"alias_kind":"arxiv_version","alias_value":"2410.02551v2","created_at":"2026-07-05T10:20:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.02551","created_at":"2026-07-05T10:20:15Z"},{"alias_kind":"pith_short_12","alias_value":"FDTDC3MCVDFL","created_at":"2026-07-05T10:20:15Z"},{"alias_kind":"pith_short_16","alias_value":"FDTDC3MCVDFL3PEY","created_at":"2026-07-05T10:20:15Z"},{"alias_kind":"pith_short_8","alias_value":"FDTDC3MC","created_at":"2026-07-05T10:20:15Z"}],"graph_snapshots":[{"event_id":"sha256:b54818bf4996b2b672c4c156cec8e0ae63f6fba1ad08a78fc8d171940a870f7b","target":"graph","created_at":"2026-07-05T10:20:15Z","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/2410.02551/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce ColaCare, a framework that enhances Electronic Health Record (EHR) modeling through multi-agent collaboration driven by Large Language Models (LLMs). Our approach seamlessly integrates domain-specific expert models with LLMs to bridge the gap between structured EHR data and text-based reasoning. Inspired by the Multidisciplinary Team (MDT) approach used in clinical settings, ColaCare employs two types of agents: DoctorAgents and a MetaAgent, which collaboratively analyze patient data. Expert models process and generate predictions from numerical EHR data, while LLM agents produce ","authors_text":"Chengwei Pan, Dehao Sui, Ewen Harrison, Huiya Zhao, Junyi Gao, Liantao Ma, Tianlong Wang, Wen Tang, Xiaochen Zheng, Yasha Wang, Yinghao Zhu, Zixiang Wang","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-03T14:55:22Z","title":"ColaCare: Enhancing Electronic Health Record Modeling through Large Language Model-Driven Multi-Agent Collaboration"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.02551","kind":"arxiv","version":2},"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:cc986fce21f3c96234234b7ecc17deb3613b39b103303d57c9fb29254079c720","target":"record","created_at":"2026-07-05T10:20:15Z","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":"1a1cbd22ac82d579c20e121546c453910946d038de75db557e4c8c7bb428069a","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-03T14:55:22Z","title_canon_sha256":"d31b5e5828a73badc2c3c3fbaba4c75a9b3f395c70545bcf715f5198c895255f"},"schema_version":"1.0","source":{"id":"2410.02551","kind":"arxiv","version":2}},"canonical_sha256":"28e6316d82a8cabdbc98e83c3661d2dba3bc5fe402a4ba92c29c9ea25f69c72f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"28e6316d82a8cabdbc98e83c3661d2dba3bc5fe402a4ba92c29c9ea25f69c72f","first_computed_at":"2026-07-05T10:20:15.777696Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:20:15.777696Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6cft6aNeVfLdccpYlBZ46N9xGZfCPmxlEfBSbPQai975D+Brbk/7JCKaAM6ahI0fOvXJW+TcLhCNwUgd/huXDw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:20:15.778341Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.02551","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cc986fce21f3c96234234b7ecc17deb3613b39b103303d57c9fb29254079c720","sha256:b54818bf4996b2b672c4c156cec8e0ae63f6fba1ad08a78fc8d171940a870f7b"],"state_sha256":"1064cafae322a50fa56f0e78135177f737b3d5113ce6999b4d5889e5ac485547"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"boLB6++BkVD403dL4xs2Axc5ycM40eO7cdKLZz9haxHMHF7bfUQew7kneDmaOW/m+6r3prwuO3cuiJI1TRRwCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T23:28:41.241789Z","bundle_sha256":"a7a3df8b4a35501acff8733f5947e06a795265c4e92145f72a6533c1f6d53cfa"}}