{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:NRKGEQJKEDBECFTMW4J22764FL","short_pith_number":"pith:NRKGEQJK","schema_version":"1.0","canonical_sha256":"6c5462412a20c241166cb713ad7fdc2ace5d5355a44f3c4965d7ad9386e96990","source":{"kind":"arxiv","id":"1707.05010","version":1},"attestation_state":"computed","paper":{"title":"Deep Learning to Attend to Risk in ICU","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Phuoc Nguyen, Svetha Venkatesh, Truyen Tran","submitted_at":"2017-07-17T06:23:20Z","abstract_excerpt":"Modeling physiological time-series in ICU is of high clinical importance. However, data collected within ICU are irregular in time and often contain missing measurements. Since absence of a measure would signify its lack of importance, the missingness is indeed informative and might reflect the decision making by the clinician. Here we propose a deep learning architecture that can effectively handle these challenges for predicting ICU mortality outcomes. The model is based on Long Short-Term Memory, and has layered attention mechanisms. At the sensing layer, the model decides whether to observ"},"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":"1707.05010","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-07-17T06:23:20Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"84d995e1744e21731d18aa27b09d4fe9a4f6ca05aeb0c6bde148abb440c1174c","abstract_canon_sha256":"2840fa75bbb8d908f3a14487566ca62d97bc116463d096493bfee35a4108ae89"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:40:11.609575Z","signature_b64":"mRuSfV49fknquyGl7KOoura0W9FhkEBQA7kVgGqn4+i/do3tWGerbU2Ts+iERytgebKCY8EN1Xzj6xLdbI//Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c5462412a20c241166cb713ad7fdc2ace5d5355a44f3c4965d7ad9386e96990","last_reissued_at":"2026-05-18T00:40:11.608968Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:40:11.608968Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Learning to Attend to Risk in ICU","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Phuoc Nguyen, Svetha Venkatesh, Truyen Tran","submitted_at":"2017-07-17T06:23:20Z","abstract_excerpt":"Modeling physiological time-series in ICU is of high clinical importance. However, data collected within ICU are irregular in time and often contain missing measurements. Since absence of a measure would signify its lack of importance, the missingness is indeed informative and might reflect the decision making by the clinician. Here we propose a deep learning architecture that can effectively handle these challenges for predicting ICU mortality outcomes. The model is based on Long Short-Term Memory, and has layered attention mechanisms. At the sensing layer, the model decides whether to observ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.05010","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":"1707.05010","created_at":"2026-05-18T00:40:11.609087+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.05010v1","created_at":"2026-05-18T00:40:11.609087+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.05010","created_at":"2026-05-18T00:40:11.609087+00:00"},{"alias_kind":"pith_short_12","alias_value":"NRKGEQJKEDBE","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_16","alias_value":"NRKGEQJKEDBECFTM","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_8","alias_value":"NRKGEQJK","created_at":"2026-05-18T12:31:34.259226+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/NRKGEQJKEDBECFTMW4J22764FL","json":"https://pith.science/pith/NRKGEQJKEDBECFTMW4J22764FL.json","graph_json":"https://pith.science/api/pith-number/NRKGEQJKEDBECFTMW4J22764FL/graph.json","events_json":"https://pith.science/api/pith-number/NRKGEQJKEDBECFTMW4J22764FL/events.json","paper":"https://pith.science/paper/NRKGEQJK"},"agent_actions":{"view_html":"https://pith.science/pith/NRKGEQJKEDBECFTMW4J22764FL","download_json":"https://pith.science/pith/NRKGEQJKEDBECFTMW4J22764FL.json","view_paper":"https://pith.science/paper/NRKGEQJK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.05010&json=true","fetch_graph":"https://pith.science/api/pith-number/NRKGEQJKEDBECFTMW4J22764FL/graph.json","fetch_events":"https://pith.science/api/pith-number/NRKGEQJKEDBECFTMW4J22764FL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NRKGEQJKEDBECFTMW4J22764FL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NRKGEQJKEDBECFTMW4J22764FL/action/storage_attestation","attest_author":"https://pith.science/pith/NRKGEQJKEDBECFTMW4J22764FL/action/author_attestation","sign_citation":"https://pith.science/pith/NRKGEQJKEDBECFTMW4J22764FL/action/citation_signature","submit_replication":"https://pith.science/pith/NRKGEQJKEDBECFTMW4J22764FL/action/replication_record"}},"created_at":"2026-05-18T00:40:11.609087+00:00","updated_at":"2026-05-18T00:40:11.609087+00:00"}