{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"TalkRL: The Reinforcement Learning Podcast","title":"Thomas Frost on Clinical RL with Natural Timings","html":"","width":"100%","height":180,"duration":5244,"description":"Dr Thomas Frost is an emergency physician based in London, UK. He is also in the final stages of completing a PhD at University College London, where he has been looking at offline reinforcement learning applied to healthcare settings.\nFeatured References\nRobust Real-Time Mortality Prediction in the Intensive Care Unit using Temporal Difference Learning\nThomas Frost, Kezhi Li, Steve Harris — ML4H Symposium, PMLR 259, 2025\nInsulin4RL: Real-Time Insulin Infusions for Offline Reinforcement Learning\nThomas Frost, Steve Harris — PhysioNet, 2026 (RRID:SCR_007345)\nThe Hidden Risks of Temporal Resampling in Clinical Reinforcement Learning\nThomas Frost, Hrisheekesh Vaidya, Steve Harris — arXiv preprint, 2026\nInsulin4RL: Real-Time Insulin Management in the Intensive Care Unit for Offline Reinforcement Learning\nThomas Frost, Steve Harris — arXiv preprint, 2026\nAdditional ReferencesThe artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care — Komorowski et al. 2018Off by a beat: the effects of temporal misalignment in reinforcement learning for sepsis treatment — Tang et al. 2026Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment — Zhang et al. 2021Where do doctors disagree? Characterizing Decision Points for Safe Reinforcement Learning in Choosing Vasopressor Treatment — Brown et al. 2025Loss of plasticity in deep continual learning — Dohare et al. 2024","thumbnail_url":"https://img.transistorcdn.com/jXB1-VPK-A9v1epzc4aG4pFxqlvo2vbQ_Ytyuar_gPI/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzIwNDcvMTcwNzk1/NDcxMS1hcnR3b3Jr/LmpwZw.webp","thumbnail_width":300,"thumbnail_height":300}