{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:SOV4LN4NJQIFBZZUCFRCHBGHW5","short_pith_number":"pith:SOV4LN4N","schema_version":"1.0","canonical_sha256":"93abc5b78d4c1050e73411622384c7b76cac66271b99d2938212bde1d05daba0","source":{"kind":"arxiv","id":"1507.07955","version":1},"attestation_state":"computed","paper":{"title":"Sparse Multidimensional Patient Modeling using Auxiliary Confidence Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Eric Heim, Milos Hauskrecht (University of Pittsburgh)","submitted_at":"2015-07-28T20:54:56Z","abstract_excerpt":"In this work, we focus on the problem of learning a classification model that performs inference on patient Electronic Health Records (EHRs). Often, a large amount of costly expert supervision is required to learn such a model. To reduce this cost, we obtain confidence labels that indicate how sure an expert is in the class labels she provides. If meaningful confidence information can be incorporated into a learning method, fewer patient instances may need to be labeled to learn an accurate model. In addition, while accuracy of predictions is important for any inference model, a model of patie"},"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":"1507.07955","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-07-28T20:54:56Z","cross_cats_sorted":[],"title_canon_sha256":"4ec680dde7180dd823ed0ea243004aba27c78349d224df871f8face9681ec5ae","abstract_canon_sha256":"5ff195f93047edf21b0fa36086ffab3235d8bfafa0c019a6b6b137d4b26ad48d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:36:08.844956Z","signature_b64":"Qrv2/fFEjTtRRsWO2dUvHVRm0wCDVhVN47j6Zc8wVzqVIQq/m1ybW+1XllMHdf3kL+vtV9StkFXtTD5Rrru7CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"93abc5b78d4c1050e73411622384c7b76cac66271b99d2938212bde1d05daba0","last_reissued_at":"2026-05-18T01:36:08.844389Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:36:08.844389Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sparse Multidimensional Patient Modeling using Auxiliary Confidence Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Eric Heim, Milos Hauskrecht (University of Pittsburgh)","submitted_at":"2015-07-28T20:54:56Z","abstract_excerpt":"In this work, we focus on the problem of learning a classification model that performs inference on patient Electronic Health Records (EHRs). Often, a large amount of costly expert supervision is required to learn such a model. To reduce this cost, we obtain confidence labels that indicate how sure an expert is in the class labels she provides. If meaningful confidence information can be incorporated into a learning method, fewer patient instances may need to be labeled to learn an accurate model. In addition, while accuracy of predictions is important for any inference model, a model of patie"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.07955","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":"1507.07955","created_at":"2026-05-18T01:36:08.844481+00:00"},{"alias_kind":"arxiv_version","alias_value":"1507.07955v1","created_at":"2026-05-18T01:36:08.844481+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.07955","created_at":"2026-05-18T01:36:08.844481+00:00"},{"alias_kind":"pith_short_12","alias_value":"SOV4LN4NJQIF","created_at":"2026-05-18T12:29:42.218222+00:00"},{"alias_kind":"pith_short_16","alias_value":"SOV4LN4NJQIFBZZU","created_at":"2026-05-18T12:29:42.218222+00:00"},{"alias_kind":"pith_short_8","alias_value":"SOV4LN4N","created_at":"2026-05-18T12:29:42.218222+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/SOV4LN4NJQIFBZZUCFRCHBGHW5","json":"https://pith.science/pith/SOV4LN4NJQIFBZZUCFRCHBGHW5.json","graph_json":"https://pith.science/api/pith-number/SOV4LN4NJQIFBZZUCFRCHBGHW5/graph.json","events_json":"https://pith.science/api/pith-number/SOV4LN4NJQIFBZZUCFRCHBGHW5/events.json","paper":"https://pith.science/paper/SOV4LN4N"},"agent_actions":{"view_html":"https://pith.science/pith/SOV4LN4NJQIFBZZUCFRCHBGHW5","download_json":"https://pith.science/pith/SOV4LN4NJQIFBZZUCFRCHBGHW5.json","view_paper":"https://pith.science/paper/SOV4LN4N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1507.07955&json=true","fetch_graph":"https://pith.science/api/pith-number/SOV4LN4NJQIFBZZUCFRCHBGHW5/graph.json","fetch_events":"https://pith.science/api/pith-number/SOV4LN4NJQIFBZZUCFRCHBGHW5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SOV4LN4NJQIFBZZUCFRCHBGHW5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SOV4LN4NJQIFBZZUCFRCHBGHW5/action/storage_attestation","attest_author":"https://pith.science/pith/SOV4LN4NJQIFBZZUCFRCHBGHW5/action/author_attestation","sign_citation":"https://pith.science/pith/SOV4LN4NJQIFBZZUCFRCHBGHW5/action/citation_signature","submit_replication":"https://pith.science/pith/SOV4LN4NJQIFBZZUCFRCHBGHW5/action/replication_record"}},"created_at":"2026-05-18T01:36:08.844481+00:00","updated_at":"2026-05-18T01:36:08.844481+00:00"}