{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:GGAMTH2ALSVDCQVXMLX6RERLDV","short_pith_number":"pith:GGAMTH2A","schema_version":"1.0","canonical_sha256":"3180c99f405caa3142b762efe8922b1d565211406621eb664e98eb25328cbab1","source":{"kind":"arxiv","id":"1703.09112","version":2},"attestation_state":"computed","paper":{"title":"Sparse Multi-Output Gaussian Processes for Medical Time Series Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Barbara E Engelhardt, Bianca Dumitrascu, Corey Chivers, Gregory Darnell, Kai Li, Li-Fang Cheng, Michael E Draugelis","submitted_at":"2017-03-27T14:38:15Z","abstract_excerpt":"In the scenario of real-time monitoring of hospital patients, high-quality inference of patients' health status using all information available from clinical covariates and lab tests is essential to enable successful medical interventions and improve patient outcomes. Developing a computational framework that can learn from observational large-scale electronic health records (EHRs) and make accurate real-time predictions is a critical step. In this work, we develop and explore a Bayesian nonparametric model based on Gaussian process (GP) regression for hospital patient monitoring. We propose M"},"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":"1703.09112","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-03-27T14:38:15Z","cross_cats_sorted":[],"title_canon_sha256":"79499258755bd9cb2c48ea9d099326d4c177bafe988bc610f2cff99cb2787b5b","abstract_canon_sha256":"c9e99aa2c57c6e2f303e55b4f550500ae3d25bef882b1928c7db056ed56f46e2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:40.797785Z","signature_b64":"SeXGwXud81gLOAYxvuETngbqTG85GKDw58r/mbd5jJh6uyyPjhKFN/2L1g/sKongXSheC1MWKrBXtNluJXOKDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3180c99f405caa3142b762efe8922b1d565211406621eb664e98eb25328cbab1","last_reissued_at":"2026-05-18T00:12:40.797196Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:40.797196Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sparse Multi-Output Gaussian Processes for Medical Time Series Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Barbara E Engelhardt, Bianca Dumitrascu, Corey Chivers, Gregory Darnell, Kai Li, Li-Fang Cheng, Michael E Draugelis","submitted_at":"2017-03-27T14:38:15Z","abstract_excerpt":"In the scenario of real-time monitoring of hospital patients, high-quality inference of patients' health status using all information available from clinical covariates and lab tests is essential to enable successful medical interventions and improve patient outcomes. Developing a computational framework that can learn from observational large-scale electronic health records (EHRs) and make accurate real-time predictions is a critical step. In this work, we develop and explore a Bayesian nonparametric model based on Gaussian process (GP) regression for hospital patient monitoring. We propose M"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.09112","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":"1703.09112","created_at":"2026-05-18T00:12:40.797281+00:00"},{"alias_kind":"arxiv_version","alias_value":"1703.09112v2","created_at":"2026-05-18T00:12:40.797281+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.09112","created_at":"2026-05-18T00:12:40.797281+00:00"},{"alias_kind":"pith_short_12","alias_value":"GGAMTH2ALSVD","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_16","alias_value":"GGAMTH2ALSVDCQVX","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_8","alias_value":"GGAMTH2A","created_at":"2026-05-18T12:31:15.632608+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/GGAMTH2ALSVDCQVXMLX6RERLDV","json":"https://pith.science/pith/GGAMTH2ALSVDCQVXMLX6RERLDV.json","graph_json":"https://pith.science/api/pith-number/GGAMTH2ALSVDCQVXMLX6RERLDV/graph.json","events_json":"https://pith.science/api/pith-number/GGAMTH2ALSVDCQVXMLX6RERLDV/events.json","paper":"https://pith.science/paper/GGAMTH2A"},"agent_actions":{"view_html":"https://pith.science/pith/GGAMTH2ALSVDCQVXMLX6RERLDV","download_json":"https://pith.science/pith/GGAMTH2ALSVDCQVXMLX6RERLDV.json","view_paper":"https://pith.science/paper/GGAMTH2A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1703.09112&json=true","fetch_graph":"https://pith.science/api/pith-number/GGAMTH2ALSVDCQVXMLX6RERLDV/graph.json","fetch_events":"https://pith.science/api/pith-number/GGAMTH2ALSVDCQVXMLX6RERLDV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GGAMTH2ALSVDCQVXMLX6RERLDV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GGAMTH2ALSVDCQVXMLX6RERLDV/action/storage_attestation","attest_author":"https://pith.science/pith/GGAMTH2ALSVDCQVXMLX6RERLDV/action/author_attestation","sign_citation":"https://pith.science/pith/GGAMTH2ALSVDCQVXMLX6RERLDV/action/citation_signature","submit_replication":"https://pith.science/pith/GGAMTH2ALSVDCQVXMLX6RERLDV/action/replication_record"}},"created_at":"2026-05-18T00:12:40.797281+00:00","updated_at":"2026-05-18T00:12:40.797281+00:00"}