{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:UINSPMBQD3HSZAPQGDX257P6QM","short_pith_number":"pith:UINSPMBQ","canonical_record":{"source":{"id":"2402.04400","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-06T20:58:36Z","cross_cats_sorted":["cs.AI","cs.CY"],"title_canon_sha256":"990987011d6d37b25020f356a13cd6b1afb3c9087172de0634b91ebc796c9903","abstract_canon_sha256":"aaf520e3fbc7386965a8433b365b5f04843512c565dde55bc2fba69bba6212c8"},"schema_version":"1.0"},"canonical_sha256":"a21b27b0301ecf2c81f030efaefdfe8317b59d30757f9a691ffba807ec76cd92","source":{"kind":"arxiv","id":"2402.04400","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.04400","created_at":"2026-07-05T08:15:45Z"},{"alias_kind":"arxiv_version","alias_value":"2402.04400v2","created_at":"2026-07-05T08:15:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.04400","created_at":"2026-07-05T08:15:45Z"},{"alias_kind":"pith_short_12","alias_value":"UINSPMBQD3HS","created_at":"2026-07-05T08:15:45Z"},{"alias_kind":"pith_short_16","alias_value":"UINSPMBQD3HSZAPQ","created_at":"2026-07-05T08:15:45Z"},{"alias_kind":"pith_short_8","alias_value":"UINSPMBQ","created_at":"2026-07-05T08:15:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:UINSPMBQD3HSZAPQGDX257P6QM","target":"record","payload":{"canonical_record":{"source":{"id":"2402.04400","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-06T20:58:36Z","cross_cats_sorted":["cs.AI","cs.CY"],"title_canon_sha256":"990987011d6d37b25020f356a13cd6b1afb3c9087172de0634b91ebc796c9903","abstract_canon_sha256":"aaf520e3fbc7386965a8433b365b5f04843512c565dde55bc2fba69bba6212c8"},"schema_version":"1.0"},"canonical_sha256":"a21b27b0301ecf2c81f030efaefdfe8317b59d30757f9a691ffba807ec76cd92","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:15:45.087630Z","signature_b64":"8F4iwVOL4+8qkavUViOxeYtUSlgZyNqqPyQD9Az068zRcPONpsTFeRrM6cP8L61aeOx+KRfNMFz/FxDXPrNBAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a21b27b0301ecf2c81f030efaefdfe8317b59d30757f9a691ffba807ec76cd92","last_reissued_at":"2026-07-05T08:15:45.087171Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:15:45.087171Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.04400","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":"2026-07-05T08:15:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9aYYJaDaA1KBCwFqSkzDA9CVCG6lMA4Wul6rs1RXwDLsdb/VcQsi37p+gL4KusfArwsaVJe++g/zHNQgzjpfBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T06:51:31.862628Z"},"content_sha256":"5e0fbb3f81f934d676062038f162da1a0b32a5dc8f117c12d92423129e7e2c84","schema_version":"1.0","event_id":"sha256:5e0fbb3f81f934d676062038f162da1a0b32a5dc8f117c12d92423129e7e2c84"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:UINSPMBQD3HSZAPQGDX257P6QM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelines","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CY"],"primary_cat":"cs.LG","authors_text":"Chao Pang, Elise L. Minto, Gamze G\\\"ursoy, George Hripcsak, Jason Patterson, Karthik Natarajan, Krishna S. Kalluri, Linying Zhang, Nishanth Parameshwar Pavinkurve, No\\'emie Elhadad, Xinzhuo Jiang","submitted_at":"2024-02-06T20:58:36Z","abstract_excerpt":"Synthetic Electronic Health Records (EHR) have emerged as a pivotal tool in advancing healthcare applications and machine learning models, particularly for researchers without direct access to healthcare data. Although existing methods, like rule-based approaches and generative adversarial networks (GANs), generate synthetic data that resembles real-world EHR data, these methods often use a tabular format, disregarding temporal dependencies in patient histories and limiting data replication. Recently, there has been a growing interest in leveraging Generative Pre-trained Transformers (GPT) for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.04400","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/2402.04400/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":"2026-07-05T08:15:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hP51jDPQext+qV24rmJWxvzHggkoCdCUbmBXEe3gM4d/8PnUvZbS2ZmXICAYMhNXq3hSiLPI7h8bPxryL9FsCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T06:51:31.863113Z"},"content_sha256":"8502b82a34055e25f2b55e00992c80b2369cc308537ea528565072d4b3aedb47","schema_version":"1.0","event_id":"sha256:8502b82a34055e25f2b55e00992c80b2369cc308537ea528565072d4b3aedb47"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UINSPMBQD3HSZAPQGDX257P6QM/bundle.json","state_url":"https://pith.science/pith/UINSPMBQD3HSZAPQGDX257P6QM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UINSPMBQD3HSZAPQGDX257P6QM/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":"2026-08-05T06:51:31Z","links":{"resolver":"https://pith.science/pith/UINSPMBQD3HSZAPQGDX257P6QM","bundle":"https://pith.science/pith/UINSPMBQD3HSZAPQGDX257P6QM/bundle.json","state":"https://pith.science/pith/UINSPMBQD3HSZAPQGDX257P6QM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UINSPMBQD3HSZAPQGDX257P6QM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:UINSPMBQD3HSZAPQGDX257P6QM","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":"aaf520e3fbc7386965a8433b365b5f04843512c565dde55bc2fba69bba6212c8","cross_cats_sorted":["cs.AI","cs.CY"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-06T20:58:36Z","title_canon_sha256":"990987011d6d37b25020f356a13cd6b1afb3c9087172de0634b91ebc796c9903"},"schema_version":"1.0","source":{"id":"2402.04400","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.04400","created_at":"2026-07-05T08:15:45Z"},{"alias_kind":"arxiv_version","alias_value":"2402.04400v2","created_at":"2026-07-05T08:15:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.04400","created_at":"2026-07-05T08:15:45Z"},{"alias_kind":"pith_short_12","alias_value":"UINSPMBQD3HS","created_at":"2026-07-05T08:15:45Z"},{"alias_kind":"pith_short_16","alias_value":"UINSPMBQD3HSZAPQ","created_at":"2026-07-05T08:15:45Z"},{"alias_kind":"pith_short_8","alias_value":"UINSPMBQ","created_at":"2026-07-05T08:15:45Z"}],"graph_snapshots":[{"event_id":"sha256:8502b82a34055e25f2b55e00992c80b2369cc308537ea528565072d4b3aedb47","target":"graph","created_at":"2026-07-05T08:15:45Z","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/2402.04400/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Synthetic Electronic Health Records (EHR) have emerged as a pivotal tool in advancing healthcare applications and machine learning models, particularly for researchers without direct access to healthcare data. Although existing methods, like rule-based approaches and generative adversarial networks (GANs), generate synthetic data that resembles real-world EHR data, these methods often use a tabular format, disregarding temporal dependencies in patient histories and limiting data replication. Recently, there has been a growing interest in leveraging Generative Pre-trained Transformers (GPT) for","authors_text":"Chao Pang, Elise L. Minto, Gamze G\\\"ursoy, George Hripcsak, Jason Patterson, Karthik Natarajan, Krishna S. Kalluri, Linying Zhang, Nishanth Parameshwar Pavinkurve, No\\'emie Elhadad, Xinzhuo Jiang","cross_cats":["cs.AI","cs.CY"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-06T20:58:36Z","title":"CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelines"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.04400","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":"sha256:5e0fbb3f81f934d676062038f162da1a0b32a5dc8f117c12d92423129e7e2c84","target":"record","created_at":"2026-07-05T08:15:45Z","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":"aaf520e3fbc7386965a8433b365b5f04843512c565dde55bc2fba69bba6212c8","cross_cats_sorted":["cs.AI","cs.CY"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-06T20:58:36Z","title_canon_sha256":"990987011d6d37b25020f356a13cd6b1afb3c9087172de0634b91ebc796c9903"},"schema_version":"1.0","source":{"id":"2402.04400","kind":"arxiv","version":2}},"canonical_sha256":"a21b27b0301ecf2c81f030efaefdfe8317b59d30757f9a691ffba807ec76cd92","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a21b27b0301ecf2c81f030efaefdfe8317b59d30757f9a691ffba807ec76cd92","first_computed_at":"2026-07-05T08:15:45.087171Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:15:45.087171Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8F4iwVOL4+8qkavUViOxeYtUSlgZyNqqPyQD9Az068zRcPONpsTFeRrM6cP8L61aeOx+KRfNMFz/FxDXPrNBAg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:15:45.087630Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.04400","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5e0fbb3f81f934d676062038f162da1a0b32a5dc8f117c12d92423129e7e2c84","sha256:8502b82a34055e25f2b55e00992c80b2369cc308537ea528565072d4b3aedb47"],"state_sha256":"dc4c9d1caf5046df2ea3a2b3739aa65f3a095b56f5d41603d43608a44d97a27f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aRs9XtZkE3w5U+q+wIs68rPvYxdqKNqIrNrwFxTdYd/DYGlGHNEcycGiY4dgfUJvVOT8wjMLznvho3eRtSgBDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T06:51:31.866206Z","bundle_sha256":"b8e205785f3374b18455932333958219b23140d17c6f56ec8319cea1a108662a"}}