{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:EWPVYKQQ6OXGO373ZK7MRQRVC4","short_pith_number":"pith:EWPVYKQQ","schema_version":"1.0","canonical_sha256":"259f5c2a10f3ae676ffbcabec8c23517316616f51172f65c4cc1d12baac812b6","source":{"kind":"arxiv","id":"2510.08350","version":3},"attestation_state":"computed","paper":{"title":"DeepEN: A Deep Reinforcement Learning Framework for Personalized Enteral Nutrition in Critical Care","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Daniel Jason Tan, Dilruk Perera, Jiayang Chen, Kay Choong See, Mengling Feng","submitted_at":"2025-10-09T15:37:56Z","abstract_excerpt":"Objective: Enteral nutrition (EN) delivery in the ICU remains suboptimal due to limited personalization and uncertainty regarding appropriate calorie, protein, and fluid targets under dynamic metabolic demands. We introduce DeepEN, a reinforcement learning (RL) framework for personalized EN optimization using electronic health record data.\n  Methods: DeepEN was trained on over 11,000 ICU patients from MIMIC-IV to generate 4-hourly, patient-specific caloric, protein, and fluid targets. The state representation incorporated demographics, comorbidities, vital signs, laboratory values, and recent "},"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":"2510.08350","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-10-09T15:37:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c2cf74c8129835e9c6a4b896fb1ce4a847acee36b3ef68065e8be987afcf1988","abstract_canon_sha256":"a924074e4dab68e08d9432aaaecc891316f16639742e9ddd9c4b36cc4f89f10c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T02:05:03.068210Z","signature_b64":"zA+ktSg7+LULBzTwcJ1oF7cHGr+p7FP3RTjmkVRLk6Q5XYnh4PbkdidGgaIh4+nST8qD6BfojAGwifjtYER3Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"259f5c2a10f3ae676ffbcabec8c23517316616f51172f65c4cc1d12baac812b6","last_reissued_at":"2026-05-26T02:05:03.067338Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T02:05:03.067338Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DeepEN: A Deep Reinforcement Learning Framework for Personalized Enteral Nutrition in Critical Care","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Daniel Jason Tan, Dilruk Perera, Jiayang Chen, Kay Choong See, Mengling Feng","submitted_at":"2025-10-09T15:37:56Z","abstract_excerpt":"Objective: Enteral nutrition (EN) delivery in the ICU remains suboptimal due to limited personalization and uncertainty regarding appropriate calorie, protein, and fluid targets under dynamic metabolic demands. We introduce DeepEN, a reinforcement learning (RL) framework for personalized EN optimization using electronic health record data.\n  Methods: DeepEN was trained on over 11,000 ICU patients from MIMIC-IV to generate 4-hourly, patient-specific caloric, protein, and fluid targets. The state representation incorporated demographics, comorbidities, vital signs, laboratory values, and recent "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.08350","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2510.08350/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2510.08350","created_at":"2026-05-26T02:05:03.067483+00:00"},{"alias_kind":"arxiv_version","alias_value":"2510.08350v3","created_at":"2026-05-26T02:05:03.067483+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.08350","created_at":"2026-05-26T02:05:03.067483+00:00"},{"alias_kind":"pith_short_12","alias_value":"EWPVYKQQ6OXG","created_at":"2026-05-26T02:05:03.067483+00:00"},{"alias_kind":"pith_short_16","alias_value":"EWPVYKQQ6OXGO373","created_at":"2026-05-26T02:05:03.067483+00:00"},{"alias_kind":"pith_short_8","alias_value":"EWPVYKQQ","created_at":"2026-05-26T02:05:03.067483+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/EWPVYKQQ6OXGO373ZK7MRQRVC4","json":"https://pith.science/pith/EWPVYKQQ6OXGO373ZK7MRQRVC4.json","graph_json":"https://pith.science/api/pith-number/EWPVYKQQ6OXGO373ZK7MRQRVC4/graph.json","events_json":"https://pith.science/api/pith-number/EWPVYKQQ6OXGO373ZK7MRQRVC4/events.json","paper":"https://pith.science/paper/EWPVYKQQ"},"agent_actions":{"view_html":"https://pith.science/pith/EWPVYKQQ6OXGO373ZK7MRQRVC4","download_json":"https://pith.science/pith/EWPVYKQQ6OXGO373ZK7MRQRVC4.json","view_paper":"https://pith.science/paper/EWPVYKQQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2510.08350&json=true","fetch_graph":"https://pith.science/api/pith-number/EWPVYKQQ6OXGO373ZK7MRQRVC4/graph.json","fetch_events":"https://pith.science/api/pith-number/EWPVYKQQ6OXGO373ZK7MRQRVC4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EWPVYKQQ6OXGO373ZK7MRQRVC4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EWPVYKQQ6OXGO373ZK7MRQRVC4/action/storage_attestation","attest_author":"https://pith.science/pith/EWPVYKQQ6OXGO373ZK7MRQRVC4/action/author_attestation","sign_citation":"https://pith.science/pith/EWPVYKQQ6OXGO373ZK7MRQRVC4/action/citation_signature","submit_replication":"https://pith.science/pith/EWPVYKQQ6OXGO373ZK7MRQRVC4/action/replication_record"}},"created_at":"2026-05-26T02:05:03.067483+00:00","updated_at":"2026-05-26T02:05:03.067483+00:00"}