报告题目：Extubation Decisions with Predictive Information for Mechanically Ventilated Patients in the ICU
报告人：谢金贵 教授
邀请人：刘洋 教授
报告时间及地点：2026年8月5日 13:30-15:00 经济管理学院 B312
报告人简况：
Prof. Dr. Jingui Xie is a W3 Professor at the Technical University of Munich (TUM) and holds the Dieter Schwarz Stiftung Associate Professorship of Business Analytics at the TUM School of Management, TUM Campus Heilbronn. He is also a core member of the Munich Data Science Institute. Prior to joining TUM in 2020, he held academic positions at Brunel University London and served as Visiting Researcher at Cambridge Judge Business School. He earned his Ph.D. in Management Science and Engineering from Tsinghua University in 2010, with a joint Ph.D. training at Columbia Business School. He previously served as Associate Professor at the School of Management, University of Science and Technology of China, where he was also the Deputy Head of the Department of Management Science and Director of the Healthcare Service Research Center.
报告内容摘要：
Weaning patients from mechanical ventilators is a crucial decision in intensive care units (ICUs), significantly affecting patient outcomes and the throughput of ICUs. This study aims to improve the current extubation protocols by incorporating predictive information on patient health conditions. We develop a discrete-time, finite-horizon Markov decision process with predictions of future state to support extubation decisions. We characterize the structure of the optimal policy and provide important insights into how predictive information can lead to different decision protocols. We demonstrate that adding predictive information is always beneficial, even if physicians place excessive trust in the predictions, as long as the predictive model is moderately accurate. Using a comprehensive data set from an ICU in a tertiary hospital in Singapore, we evaluate the effectiveness of various policies and demonstrate that incorporating predictive information can reduce ICU length of stay by up to 3.4% and, simultaneously, decrease the extubation failure rate by up to 20.3%, compared with the optimal policy that does not utilize prediction. These benefits are more significant for patients with poor initial conditions upon ICU admission. Both our analytical and numerical findings suggest that predictive information is particularly valuable in identifying patients who could benefit from continued intubation, thereby allowing for personalized and delayed extubation for these patients.
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