{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:FCEMOSARMVJOBS7IRMLHDMMJWP","short_pith_number":"pith:FCEMOSAR","schema_version":"1.0","canonical_sha256":"2888c748116552e0cbe88b1671b189b3f81e2ac2d079ae442e5d133cda2b5837","source":{"kind":"arxiv","id":"1811.12276","version":2},"attestation_state":"computed","paper":{"title":"Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Aaron Colak, Arun Ravi, Borui Zhang, Busra Celikkaya, Daniel Navarro, Matthieu Liger, Mengqi Jin, Mohammad Taha Bahadori, Mohammed Khalilia, Parminder Bhatia, Ram Bhakta, Selvan Senthivel, Taha Kass-hout, Tiberiu Doman","submitted_at":"2018-11-29T16:10:41Z","abstract_excerpt":"Clinical text provides essential information to estimate the acuity of a patient during hospital stays in addition to structured clinical data. In this study, we explore how clinical text can complement a clinical predictive learning task. We leverage an internal medical natural language processing service to perform named entity extraction and negation detection on clinical notes and compose selected entities into a new text corpus to train document representations. We then propose a multimodal neural network to jointly train time series signals and unstructured clinical text representations "},"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":"1811.12276","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-11-29T16:10:41Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"4e6d15a6a7fd324193809f06988487967149f45c4a8e05ffbbff306be67de82b","abstract_canon_sha256":"75d3f993ce67ca644ab0ddd057b96732558e11f002c039910c7b5dec65817a72"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:13.604093Z","signature_b64":"8mM5tj4DlwmiAK0+GnQIrVFAUbE40wqm7Y9Dr9YfAa/OCQSeENN+/t4wLaVMhIRqk5wgDuq5jwlpmihvcrVcDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2888c748116552e0cbe88b1671b189b3f81e2ac2d079ae442e5d133cda2b5837","last_reissued_at":"2026-05-17T23:59:13.603645Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:13.603645Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Aaron Colak, Arun Ravi, Borui Zhang, Busra Celikkaya, Daniel Navarro, Matthieu Liger, Mengqi Jin, Mohammad Taha Bahadori, Mohammed Khalilia, Parminder Bhatia, Ram Bhakta, Selvan Senthivel, Taha Kass-hout, Tiberiu Doman","submitted_at":"2018-11-29T16:10:41Z","abstract_excerpt":"Clinical text provides essential information to estimate the acuity of a patient during hospital stays in addition to structured clinical data. In this study, we explore how clinical text can complement a clinical predictive learning task. We leverage an internal medical natural language processing service to perform named entity extraction and negation detection on clinical notes and compose selected entities into a new text corpus to train document representations. We then propose a multimodal neural network to jointly train time series signals and unstructured clinical text representations "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.12276","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":"1811.12276","created_at":"2026-05-17T23:59:13.603720+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.12276v2","created_at":"2026-05-17T23:59:13.603720+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.12276","created_at":"2026-05-17T23:59:13.603720+00:00"},{"alias_kind":"pith_short_12","alias_value":"FCEMOSARMVJO","created_at":"2026-05-18T12:32:22.470017+00:00"},{"alias_kind":"pith_short_16","alias_value":"FCEMOSARMVJOBS7I","created_at":"2026-05-18T12:32:22.470017+00:00"},{"alias_kind":"pith_short_8","alias_value":"FCEMOSAR","created_at":"2026-05-18T12:32:22.470017+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/FCEMOSARMVJOBS7IRMLHDMMJWP","json":"https://pith.science/pith/FCEMOSARMVJOBS7IRMLHDMMJWP.json","graph_json":"https://pith.science/api/pith-number/FCEMOSARMVJOBS7IRMLHDMMJWP/graph.json","events_json":"https://pith.science/api/pith-number/FCEMOSARMVJOBS7IRMLHDMMJWP/events.json","paper":"https://pith.science/paper/FCEMOSAR"},"agent_actions":{"view_html":"https://pith.science/pith/FCEMOSARMVJOBS7IRMLHDMMJWP","download_json":"https://pith.science/pith/FCEMOSARMVJOBS7IRMLHDMMJWP.json","view_paper":"https://pith.science/paper/FCEMOSAR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.12276&json=true","fetch_graph":"https://pith.science/api/pith-number/FCEMOSARMVJOBS7IRMLHDMMJWP/graph.json","fetch_events":"https://pith.science/api/pith-number/FCEMOSARMVJOBS7IRMLHDMMJWP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FCEMOSARMVJOBS7IRMLHDMMJWP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FCEMOSARMVJOBS7IRMLHDMMJWP/action/storage_attestation","attest_author":"https://pith.science/pith/FCEMOSARMVJOBS7IRMLHDMMJWP/action/author_attestation","sign_citation":"https://pith.science/pith/FCEMOSARMVJOBS7IRMLHDMMJWP/action/citation_signature","submit_replication":"https://pith.science/pith/FCEMOSARMVJOBS7IRMLHDMMJWP/action/replication_record"}},"created_at":"2026-05-17T23:59:13.603720+00:00","updated_at":"2026-05-17T23:59:13.603720+00:00"}