{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CPPETYQZIZSZNDJL2TJHB7JXUG","short_pith_number":"pith:CPPETYQZ","schema_version":"1.0","canonical_sha256":"13de49e2194665968d2bd4d270fd37a182126c2c696c11e6bd80fa45f8ab39e6","source":{"kind":"arxiv","id":"2504.07373","version":1},"attestation_state":"computed","paper":{"title":"ChronoFormer: Time-Aware Transformer Architectures for Structured Clinical Event Modeling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Shi Li, Yuanyun Zhang","submitted_at":"2025-04-10T01:25:41Z","abstract_excerpt":"The temporal complexity of electronic health record (EHR) data presents significant challenges for predicting clinical outcomes using machine learning. This paper proposes ChronoFormer, an innovative transformer based architecture specifically designed to encode and leverage temporal dependencies in longitudinal patient data. ChronoFormer integrates temporal embeddings, hierarchical attention mechanisms, and domain specific masking techniques. Extensive experiments conducted on three benchmark tasks mortality prediction, readmission prediction, and long term comorbidity onset demonstrate subst"},"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":"2504.07373","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-10T01:25:41Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9303af59c655260ded2b78b3688dc71838083778cd65795eb0258706d69e78d0","abstract_canon_sha256":"46cd04f3296f33b8107f034ecd740fe3c33272b9d1dceb1fa2b8a6bdb0b1bf0b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:47:07.719501Z","signature_b64":"fUDkSzaFy4+gTElpM52f0pxqd1TKAWNLCy1fDHlg6xDUvprSAFiMTqWu/9gdDQ/CYakLO3qEJwaIGGTurZvtBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"13de49e2194665968d2bd4d270fd37a182126c2c696c11e6bd80fa45f8ab39e6","last_reissued_at":"2026-07-05T10:47:07.718955Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:47:07.718955Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ChronoFormer: Time-Aware Transformer Architectures for Structured Clinical Event Modeling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Shi Li, Yuanyun Zhang","submitted_at":"2025-04-10T01:25:41Z","abstract_excerpt":"The temporal complexity of electronic health record (EHR) data presents significant challenges for predicting clinical outcomes using machine learning. This paper proposes ChronoFormer, an innovative transformer based architecture specifically designed to encode and leverage temporal dependencies in longitudinal patient data. ChronoFormer integrates temporal embeddings, hierarchical attention mechanisms, and domain specific masking techniques. Extensive experiments conducted on three benchmark tasks mortality prediction, readmission prediction, and long term comorbidity onset demonstrate subst"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.07373","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/2504.07373/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":"2504.07373","created_at":"2026-07-05T10:47:07.719012+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.07373v1","created_at":"2026-07-05T10:47:07.719012+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.07373","created_at":"2026-07-05T10:47:07.719012+00:00"},{"alias_kind":"pith_short_12","alias_value":"CPPETYQZIZSZ","created_at":"2026-07-05T10:47:07.719012+00:00"},{"alias_kind":"pith_short_16","alias_value":"CPPETYQZIZSZNDJL","created_at":"2026-07-05T10:47:07.719012+00:00"},{"alias_kind":"pith_short_8","alias_value":"CPPETYQZ","created_at":"2026-07-05T10:47:07.719012+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24604","citing_title":"Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04175","citing_title":"Uncertainty-Aware Foundation Models for Clinical Data","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08685","citing_title":"Event Fields: Learning Latent Event Structure for Waveform Foundation Models","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09765","citing_title":"WISTERIA: Learning Clinical Representations from Noisy Supervision via Multi-View Consistency in Electronic Health Records","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21623","citing_title":"A-THENA: Early Intrusion Detection for IoT with Time-Aware Hybrid Encoding and Network-Specific Augmentation","ref_index":77,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18570","citing_title":"A multimodal and temporal foundation model for virtual patient representations at healthcare system scale","ref_index":32,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CPPETYQZIZSZNDJL2TJHB7JXUG","json":"https://pith.science/pith/CPPETYQZIZSZNDJL2TJHB7JXUG.json","graph_json":"https://pith.science/api/pith-number/CPPETYQZIZSZNDJL2TJHB7JXUG/graph.json","events_json":"https://pith.science/api/pith-number/CPPETYQZIZSZNDJL2TJHB7JXUG/events.json","paper":"https://pith.science/paper/CPPETYQZ"},"agent_actions":{"view_html":"https://pith.science/pith/CPPETYQZIZSZNDJL2TJHB7JXUG","download_json":"https://pith.science/pith/CPPETYQZIZSZNDJL2TJHB7JXUG.json","view_paper":"https://pith.science/paper/CPPETYQZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.07373&json=true","fetch_graph":"https://pith.science/api/pith-number/CPPETYQZIZSZNDJL2TJHB7JXUG/graph.json","fetch_events":"https://pith.science/api/pith-number/CPPETYQZIZSZNDJL2TJHB7JXUG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CPPETYQZIZSZNDJL2TJHB7JXUG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CPPETYQZIZSZNDJL2TJHB7JXUG/action/storage_attestation","attest_author":"https://pith.science/pith/CPPETYQZIZSZNDJL2TJHB7JXUG/action/author_attestation","sign_citation":"https://pith.science/pith/CPPETYQZIZSZNDJL2TJHB7JXUG/action/citation_signature","submit_replication":"https://pith.science/pith/CPPETYQZIZSZNDJL2TJHB7JXUG/action/replication_record"}},"created_at":"2026-07-05T10:47:07.719012+00:00","updated_at":"2026-07-05T10:47:07.719012+00:00"}