{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:6TLXZ5ZDS57BPBF6BIGW4BXJJK","short_pith_number":"pith:6TLXZ5ZD","schema_version":"1.0","canonical_sha256":"f4d77cf723977e1784be0a0d6e06e94aafcc6b16e607d8912b0cceb6ea43ee8d","source":{"kind":"arxiv","id":"2312.10308","version":4},"attestation_state":"computed","paper":{"title":"Event-Based Contrastive Learning for Medical Time Series","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Aparna Balagopalan, Bryan Jangeesingh, Collin Stultz, Hyewon Jeong, Marzyeh Ghassemi, Matthew McDermott, Nassim Oufattole","submitted_at":"2023-12-16T03:50:24Z","abstract_excerpt":"In clinical practice, one often needs to identify whether a patient is at high risk of adverse outcomes after some key medical event. For example, quantifying the risk of adverse outcomes after an acute cardiovascular event helps healthcare providers identify those patients at the highest risk of poor outcomes; i.e., patients who benefit from invasive therapies that can lower their risk. Assessing the risk of adverse outcomes, however, is challenging due to the complexity, variability, and heterogeneity of longitudinal medical data, especially for individuals suffering from chronic diseases li"},"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":"2312.10308","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-12-16T03:50:24Z","cross_cats_sorted":[],"title_canon_sha256":"be8cfc9ccdbd7dcf9fbbe557dd424115ae40117e5a99e1f3a00fe59d5af5ecb6","abstract_canon_sha256":"da8d6304a25ab34160b9ad708836cca4f2106f5381736a6626de15dddf131aea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:53:18.909082Z","signature_b64":"f4TRzvFbuGIxNKlHYuTsVn1N/naS5ZHj+Yd96VSLHajcq7NN3l1BIVnit/Z4FEHZwDHkws1rg6HkxPPSBf1yBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4d77cf723977e1784be0a0d6e06e94aafcc6b16e607d8912b0cceb6ea43ee8d","last_reissued_at":"2026-07-05T08:53:18.908603Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:53:18.908603Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Event-Based Contrastive Learning for Medical Time Series","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Aparna Balagopalan, Bryan Jangeesingh, Collin Stultz, Hyewon Jeong, Marzyeh Ghassemi, Matthew McDermott, Nassim Oufattole","submitted_at":"2023-12-16T03:50:24Z","abstract_excerpt":"In clinical practice, one often needs to identify whether a patient is at high risk of adverse outcomes after some key medical event. For example, quantifying the risk of adverse outcomes after an acute cardiovascular event helps healthcare providers identify those patients at the highest risk of poor outcomes; i.e., patients who benefit from invasive therapies that can lower their risk. Assessing the risk of adverse outcomes, however, is challenging due to the complexity, variability, and heterogeneity of longitudinal medical data, especially for individuals suffering from chronic diseases li"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.10308","kind":"arxiv","version":4},"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/2312.10308/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":"2312.10308","created_at":"2026-07-05T08:53:18.908663+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.10308v4","created_at":"2026-07-05T08:53:18.908663+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.10308","created_at":"2026-07-05T08:53:18.908663+00:00"},{"alias_kind":"pith_short_12","alias_value":"6TLXZ5ZDS57B","created_at":"2026-07-05T08:53:18.908663+00:00"},{"alias_kind":"pith_short_16","alias_value":"6TLXZ5ZDS57BPBF6","created_at":"2026-07-05T08:53:18.908663+00:00"},{"alias_kind":"pith_short_8","alias_value":"6TLXZ5ZD","created_at":"2026-07-05T08:53:18.908663+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2602.00520","citing_title":"NEST: Nested Event Stream Transformer for Sequences of Multisets","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6TLXZ5ZDS57BPBF6BIGW4BXJJK","json":"https://pith.science/pith/6TLXZ5ZDS57BPBF6BIGW4BXJJK.json","graph_json":"https://pith.science/api/pith-number/6TLXZ5ZDS57BPBF6BIGW4BXJJK/graph.json","events_json":"https://pith.science/api/pith-number/6TLXZ5ZDS57BPBF6BIGW4BXJJK/events.json","paper":"https://pith.science/paper/6TLXZ5ZD"},"agent_actions":{"view_html":"https://pith.science/pith/6TLXZ5ZDS57BPBF6BIGW4BXJJK","download_json":"https://pith.science/pith/6TLXZ5ZDS57BPBF6BIGW4BXJJK.json","view_paper":"https://pith.science/paper/6TLXZ5ZD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.10308&json=true","fetch_graph":"https://pith.science/api/pith-number/6TLXZ5ZDS57BPBF6BIGW4BXJJK/graph.json","fetch_events":"https://pith.science/api/pith-number/6TLXZ5ZDS57BPBF6BIGW4BXJJK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6TLXZ5ZDS57BPBF6BIGW4BXJJK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6TLXZ5ZDS57BPBF6BIGW4BXJJK/action/storage_attestation","attest_author":"https://pith.science/pith/6TLXZ5ZDS57BPBF6BIGW4BXJJK/action/author_attestation","sign_citation":"https://pith.science/pith/6TLXZ5ZDS57BPBF6BIGW4BXJJK/action/citation_signature","submit_replication":"https://pith.science/pith/6TLXZ5ZDS57BPBF6BIGW4BXJJK/action/replication_record"}},"created_at":"2026-07-05T08:53:18.908663+00:00","updated_at":"2026-07-05T08:53:18.908663+00:00"}