{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:AYF4TIPTHX4TZCZMBWYSF3XI7T","short_pith_number":"pith:AYF4TIPT","schema_version":"1.0","canonical_sha256":"060bc9a1f33df93c8b2c0db122eee8fce2d2c50744590802905f8bec02cd71a4","source":{"kind":"arxiv","id":"1306.2094","version":1},"attestation_state":"computed","paper":{"title":"Predicting Risk-of-Readmission for Congestive Heart Failure Patients: A Multi-Layer Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"cs.LG","authors_text":"Ankur Teredesai, David Hazel, Jayshree Agarwal, Kiyana Zolfaghar, Lester Reed, Naren Meadem, Nele Verbiest, Paul Amoroso, Senjuti Basu Roy, Si-Chi Chin","submitted_at":"2013-06-10T03:18:25Z","abstract_excerpt":"Mitigating risk-of-readmission of Congestive Heart Failure (CHF) patients within 30 days of discharge is important because such readmissions are not only expensive but also critical indicator of provider care and quality of treatment. Accurately predicting the risk-of-readmission may allow hospitals to identify high-risk patients and eventually improve quality of care by identifying factors that contribute to such readmissions in many scenarios. In this paper, we investigate the problem of predicting risk-of-readmission as a supervised learning problem, using a multi-layer classification appro"},"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":"1306.2094","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-06-10T03:18:25Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"b5cc692dcc340ddf896d0d2e501a3a4c8c2f16d29cb50dddbc1e8bd196db0b47","abstract_canon_sha256":"c01797ef1253c617fa316bbe222124c9be5542d257a5e19d5431451bb9ab9694"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:21:22.419672Z","signature_b64":"nIWhcNX0qJZrRcRTkGVHNT2x+687MBesRWa++oGA3kYKAK/ZX9ENYFs72HtobviFOITgfRJ1iFzReSufC87oCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"060bc9a1f33df93c8b2c0db122eee8fce2d2c50744590802905f8bec02cd71a4","last_reissued_at":"2026-05-18T03:21:22.418950Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:21:22.418950Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Predicting Risk-of-Readmission for Congestive Heart Failure Patients: A Multi-Layer Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"cs.LG","authors_text":"Ankur Teredesai, David Hazel, Jayshree Agarwal, Kiyana Zolfaghar, Lester Reed, Naren Meadem, Nele Verbiest, Paul Amoroso, Senjuti Basu Roy, Si-Chi Chin","submitted_at":"2013-06-10T03:18:25Z","abstract_excerpt":"Mitigating risk-of-readmission of Congestive Heart Failure (CHF) patients within 30 days of discharge is important because such readmissions are not only expensive but also critical indicator of provider care and quality of treatment. Accurately predicting the risk-of-readmission may allow hospitals to identify high-risk patients and eventually improve quality of care by identifying factors that contribute to such readmissions in many scenarios. In this paper, we investigate the problem of predicting risk-of-readmission as a supervised learning problem, using a multi-layer classification appro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1306.2094","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":""},"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":"1306.2094","created_at":"2026-05-18T03:21:22.419067+00:00"},{"alias_kind":"arxiv_version","alias_value":"1306.2094v1","created_at":"2026-05-18T03:21:22.419067+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1306.2094","created_at":"2026-05-18T03:21:22.419067+00:00"},{"alias_kind":"pith_short_12","alias_value":"AYF4TIPTHX4T","created_at":"2026-05-18T12:27:38.830355+00:00"},{"alias_kind":"pith_short_16","alias_value":"AYF4TIPTHX4TZCZM","created_at":"2026-05-18T12:27:38.830355+00:00"},{"alias_kind":"pith_short_8","alias_value":"AYF4TIPT","created_at":"2026-05-18T12:27:38.830355+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/AYF4TIPTHX4TZCZMBWYSF3XI7T","json":"https://pith.science/pith/AYF4TIPTHX4TZCZMBWYSF3XI7T.json","graph_json":"https://pith.science/api/pith-number/AYF4TIPTHX4TZCZMBWYSF3XI7T/graph.json","events_json":"https://pith.science/api/pith-number/AYF4TIPTHX4TZCZMBWYSF3XI7T/events.json","paper":"https://pith.science/paper/AYF4TIPT"},"agent_actions":{"view_html":"https://pith.science/pith/AYF4TIPTHX4TZCZMBWYSF3XI7T","download_json":"https://pith.science/pith/AYF4TIPTHX4TZCZMBWYSF3XI7T.json","view_paper":"https://pith.science/paper/AYF4TIPT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1306.2094&json=true","fetch_graph":"https://pith.science/api/pith-number/AYF4TIPTHX4TZCZMBWYSF3XI7T/graph.json","fetch_events":"https://pith.science/api/pith-number/AYF4TIPTHX4TZCZMBWYSF3XI7T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AYF4TIPTHX4TZCZMBWYSF3XI7T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AYF4TIPTHX4TZCZMBWYSF3XI7T/action/storage_attestation","attest_author":"https://pith.science/pith/AYF4TIPTHX4TZCZMBWYSF3XI7T/action/author_attestation","sign_citation":"https://pith.science/pith/AYF4TIPTHX4TZCZMBWYSF3XI7T/action/citation_signature","submit_replication":"https://pith.science/pith/AYF4TIPTHX4TZCZMBWYSF3XI7T/action/replication_record"}},"created_at":"2026-05-18T03:21:22.419067+00:00","updated_at":"2026-05-18T03:21:22.419067+00:00"}