{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:M3632LGBIWRIHUUC35QADMAWCF","short_pith_number":"pith:M3632LGB","schema_version":"1.0","canonical_sha256":"66fdbd2cc145a283d282df6001b016117a385497a08d00c6c188053660d909af","source":{"kind":"arxiv","id":"1903.00197","version":2},"attestation_state":"computed","paper":{"title":"Outcome-Driven Clustering of Acute Coronary Syndrome Patients using Multi-Task Neural Network with Attention","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"q-bio.QM","authors_text":"Changsheng Ma, Eryu Xia, Jian Li, Jian Sheng, Jianzeng Dong, Jing Mei, Shaochun Li, Suijun Tong, Wen Sun, Xin Du, Zhiqing Kang","submitted_at":"2019-03-01T08:20:28Z","abstract_excerpt":"Cluster analysis aims at separating patients into phenotypically heterogenous groups and defining therapeutically homogeneous patient subclasses. It is an important approach in data-driven disease classification and subtyping. Acute coronary syndrome (ACS) is a syndrome due to sudden decrease of coronary artery blood flow, where disease classification would help to inform therapeutic strategies and provide prognostic insights. Here we conducted outcome-driven cluster analysis of ACS patients, which jointly considers treatment and patient outcome as indicators for patient state. Multi-task neur"},"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":"1903.00197","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2019-03-01T08:20:28Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"829334b931d1c3b083b2069f3b40a9bc86aa6b30004db5626840b0cc36a43964","abstract_canon_sha256":"7afde55c6b8be2eed3d3fda52ffa75938964a53220ae5a86deefd83e3ea84054"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:04.478151Z","signature_b64":"Q/o3Dsxrz8q/tlHVrqcoADku6ICBpoiME4Y/y3KmDwoVq3NFWzcJWwfubOMQWtDuhDrDJr/zRyx+S4VJJtp/BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"66fdbd2cc145a283d282df6001b016117a385497a08d00c6c188053660d909af","last_reissued_at":"2026-05-17T23:50:04.477658Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:04.477658Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Outcome-Driven Clustering of Acute Coronary Syndrome Patients using Multi-Task Neural Network with Attention","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"q-bio.QM","authors_text":"Changsheng Ma, Eryu Xia, Jian Li, Jian Sheng, Jianzeng Dong, Jing Mei, Shaochun Li, Suijun Tong, Wen Sun, Xin Du, Zhiqing Kang","submitted_at":"2019-03-01T08:20:28Z","abstract_excerpt":"Cluster analysis aims at separating patients into phenotypically heterogenous groups and defining therapeutically homogeneous patient subclasses. It is an important approach in data-driven disease classification and subtyping. Acute coronary syndrome (ACS) is a syndrome due to sudden decrease of coronary artery blood flow, where disease classification would help to inform therapeutic strategies and provide prognostic insights. Here we conducted outcome-driven cluster analysis of ACS patients, which jointly considers treatment and patient outcome as indicators for patient state. Multi-task neur"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.00197","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":""},"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":"1903.00197","created_at":"2026-05-17T23:50:04.477722+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.00197v2","created_at":"2026-05-17T23:50:04.477722+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.00197","created_at":"2026-05-17T23:50:04.477722+00:00"},{"alias_kind":"pith_short_12","alias_value":"M3632LGBIWRI","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"M3632LGBIWRIHUUC","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"M3632LGB","created_at":"2026-05-18T12:33:21.387695+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/M3632LGBIWRIHUUC35QADMAWCF","json":"https://pith.science/pith/M3632LGBIWRIHUUC35QADMAWCF.json","graph_json":"https://pith.science/api/pith-number/M3632LGBIWRIHUUC35QADMAWCF/graph.json","events_json":"https://pith.science/api/pith-number/M3632LGBIWRIHUUC35QADMAWCF/events.json","paper":"https://pith.science/paper/M3632LGB"},"agent_actions":{"view_html":"https://pith.science/pith/M3632LGBIWRIHUUC35QADMAWCF","download_json":"https://pith.science/pith/M3632LGBIWRIHUUC35QADMAWCF.json","view_paper":"https://pith.science/paper/M3632LGB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.00197&json=true","fetch_graph":"https://pith.science/api/pith-number/M3632LGBIWRIHUUC35QADMAWCF/graph.json","fetch_events":"https://pith.science/api/pith-number/M3632LGBIWRIHUUC35QADMAWCF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M3632LGBIWRIHUUC35QADMAWCF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M3632LGBIWRIHUUC35QADMAWCF/action/storage_attestation","attest_author":"https://pith.science/pith/M3632LGBIWRIHUUC35QADMAWCF/action/author_attestation","sign_citation":"https://pith.science/pith/M3632LGBIWRIHUUC35QADMAWCF/action/citation_signature","submit_replication":"https://pith.science/pith/M3632LGBIWRIHUUC35QADMAWCF/action/replication_record"}},"created_at":"2026-05-17T23:50:04.477722+00:00","updated_at":"2026-05-17T23:50:04.477722+00:00"}