{"type":"ScholarlyArticle","title":"Uncertainty-Guided Adaptive Conservative Offline Reinforcement Learning for Safer Mechanical Ventilation","authors":[{"name":"Huidong Liu","slug":"huidong-liu","given":"Huidong","family":"Liu","affiliation":""},{"name":"Hang Yu","slug":"hang-yu","given":"Hang","family":"Yu","affiliation":""},{"name":"Qiyang Zhang","slug":"qiyang-zhang","given":"Qiyang","family":"Zhang","affiliation":""},{"name":"Jiarui Dou","slug":"jiarui-dou","given":"Jiarui","family":"Dou","affiliation":""},{"name":"Xianlei Long","slug":"xianlei-long","given":"Xianlei","family":"Long","affiliation":""},{"name":"Jiantao Shi","slug":"jiantao-shi","given":"Jiantao","family":"Shi","affiliation":""},{"name":"Fuqiang Gu","slug":"fuqiang-gu","given":"Fuqiang","family":"Gu","affiliation":""}],"abstract":"Mechanical ventilation (MV) is essential in intensive care units (ICUs), yet conventional protocols lack personalization and risk harmful over- or under-ventilation. Offline reinforcement learning (ORL) enables policy optimization from retrospective clinical data without unsafe online interaction, but existing methods are highly sensitive to distributional shift and out-of-distribution (OOD) actions, limiting their reliability in complex clinical settings. To address these challenges, We propose UBER-CQL (Uncertainty-Balanced Exploration and Robust Conservative Q-Learning), a robust ORL algorithm for safe decision-making under dataset shift. UBER-CQL integrates heteroscedastic Bayesian neural networks with conservative Q-learning to model posterior Q-value uncertainty, which is used to adaptively penalize unreliable high-risk actions while maintaining performance within the data support. We further design numerically stable objectives for conservative Bayesian value estimation. Experiments on in-distribution and OOD subsets of MIMIC-III and eICU demonstrate that UBER-CQL outperforms state-of-the-art ORL and clinician baselines, producing safer and more effective MV strategies.","doi":"10.24963/ijcai.2026/755","url":"https://ijcai.org/proceedings/2026/755","pdf":"https://ijcai.org/proceedings/2026/0755.pdf","markdown":"https://ijcai.org/proceedings/2026/755.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/755","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI and Health","track":"AI and Health","number":755,"pages":{"first":6788,"last":6796},"keywords":["Precision medicine: Precision medicine","Medical diagnosis: Medical diagnosis","Clinical decision support system: Clinical decision support system","Treatment recommendation: Treatment recommendation"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}