{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:LN3GV6FVP6WVRRPKQC2TAT422X","short_pith_number":"pith:LN3GV6FV","schema_version":"1.0","canonical_sha256":"5b766af8b57fad58c5ea80b5304f9ad5e4d549adb16e93f78430c16a035177ae","source":{"kind":"arxiv","id":"2210.02552","version":1},"attestation_state":"computed","paper":{"title":"Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Doina Precup, Elaine Lau, Flemming Kondrup, Jacob Shkrob, My Duc Tran, Nathan de Lara, Sumana Basu, Thomas Jiralerspong","submitted_at":"2022-10-05T20:41:17Z","abstract_excerpt":"Mechanical ventilation is a key form of life support for patients with pulmonary impairment. Healthcare workers are required to continuously adjust ventilator settings for each patient, a challenging and time consuming task. Hence, it would be beneficial to develop an automated decision support tool to optimize ventilation treatment. We present DeepVent, a Conservative Q-Learning (CQL) based offline Deep Reinforcement Learning (DRL) agent that learns to predict the optimal ventilator parameters for a patient to promote 90 day survival. We design a clinically relevant intermediate reward that e"},"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":"2210.02552","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-10-05T20:41:17Z","cross_cats_sorted":[],"title_canon_sha256":"9cac9c74840a259fdeea24cdd04c5a911a3ca0318317bc817e68377f768a7c2e","abstract_canon_sha256":"970c0444e039ed2792a9ac571c43e2111fa24fd478259a2d95ae1f8e0dbb5420"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:03:53.890993Z","signature_b64":"lZ4ddZRlAeUTpSR1I0SHh88MGiGMW0UeTj4ulfUzjSjvmu7lHUg1YNyi0VfgY3g0exA2DrU+RviuVNHpenc0DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5b766af8b57fad58c5ea80b5304f9ad5e4d549adb16e93f78430c16a035177ae","last_reissued_at":"2026-07-05T05:03:53.890650Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:03:53.890650Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Doina Precup, Elaine Lau, Flemming Kondrup, Jacob Shkrob, My Duc Tran, Nathan de Lara, Sumana Basu, Thomas Jiralerspong","submitted_at":"2022-10-05T20:41:17Z","abstract_excerpt":"Mechanical ventilation is a key form of life support for patients with pulmonary impairment. Healthcare workers are required to continuously adjust ventilator settings for each patient, a challenging and time consuming task. Hence, it would be beneficial to develop an automated decision support tool to optimize ventilation treatment. We present DeepVent, a Conservative Q-Learning (CQL) based offline Deep Reinforcement Learning (DRL) agent that learns to predict the optimal ventilator parameters for a patient to promote 90 day survival. We design a clinically relevant intermediate reward that e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.02552","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2210.02552/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":"2210.02552","created_at":"2026-07-05T05:03:53.890705+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.02552v1","created_at":"2026-07-05T05:03:53.890705+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.02552","created_at":"2026-07-05T05:03:53.890705+00:00"},{"alias_kind":"pith_short_12","alias_value":"LN3GV6FVP6WV","created_at":"2026-07-05T05:03:53.890705+00:00"},{"alias_kind":"pith_short_16","alias_value":"LN3GV6FVP6WVRRPK","created_at":"2026-07-05T05:03:53.890705+00:00"},{"alias_kind":"pith_short_8","alias_value":"LN3GV6FV","created_at":"2026-07-05T05:03:53.890705+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/LN3GV6FVP6WVRRPKQC2TAT422X","json":"https://pith.science/pith/LN3GV6FVP6WVRRPKQC2TAT422X.json","graph_json":"https://pith.science/api/pith-number/LN3GV6FVP6WVRRPKQC2TAT422X/graph.json","events_json":"https://pith.science/api/pith-number/LN3GV6FVP6WVRRPKQC2TAT422X/events.json","paper":"https://pith.science/paper/LN3GV6FV"},"agent_actions":{"view_html":"https://pith.science/pith/LN3GV6FVP6WVRRPKQC2TAT422X","download_json":"https://pith.science/pith/LN3GV6FVP6WVRRPKQC2TAT422X.json","view_paper":"https://pith.science/paper/LN3GV6FV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.02552&json=true","fetch_graph":"https://pith.science/api/pith-number/LN3GV6FVP6WVRRPKQC2TAT422X/graph.json","fetch_events":"https://pith.science/api/pith-number/LN3GV6FVP6WVRRPKQC2TAT422X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LN3GV6FVP6WVRRPKQC2TAT422X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LN3GV6FVP6WVRRPKQC2TAT422X/action/storage_attestation","attest_author":"https://pith.science/pith/LN3GV6FVP6WVRRPKQC2TAT422X/action/author_attestation","sign_citation":"https://pith.science/pith/LN3GV6FVP6WVRRPKQC2TAT422X/action/citation_signature","submit_replication":"https://pith.science/pith/LN3GV6FVP6WVRRPKQC2TAT422X/action/replication_record"}},"created_at":"2026-07-05T05:03:53.890705+00:00","updated_at":"2026-07-05T05:03:53.890705+00:00"}