An Artificial Intelligence Framework for Modeling Physiologic and Pharmacologic Dynamics in Anesthesia

About This Grant

Project Summary/Abstract Over 300 million people undergo surgery annually, each made possible by modern anesthesia, a complex series of medical interventions that induce a controlled, unresponsive state. In the perioperative period, anesthesia providers are faced with increasingly complex medical decisions as patient care and surgical procedures continue to evolve. Medication errors and adverse events comprise a large proportion of anesthetic adverse events, ranging from 5-28%, in part stemming from significant individual physiological variability and poorly understood drug interactions. Traditional methods for improving care, such as randomized control trials, are impractical due to the number of combinations of interventions during one anesthetic. To address this gap, the development of computational tools that harness the wealth of generated perioperative data presents a significant opportunity to support anesthesia practice. However, current approaches have been limited by low outcome generalizability, small patient datasets, manual simplification of complex time-dependent features, and poor clinical actionability. To address these challenges, this study hypothesizes that global, time-aware artificial intelligence modeling approaches can fundamentally capture the physiologic and pharmacologic principles in anesthetic care. The study will be accomplished via three specific aims: 1) the construction of a global foundation model with reusable representations of patients and their procedures for multiple, simultaneous output prediction, 2) real-time network-based modeling of patient physiology in response to combinatorial drug interventions, and 3) development of a causal inference framework to support identifying optimal, dynamic treatment regimens. The proposed research is innovative because it leverages large-scale perioperative datasets with the development and application of sophisticated artificial intelligence and statistical methods to model the intricacies of perioperative practice. The research project is significant because successful development of these methods to teach machines these fundamental principles has the potential to serve as a versatile foundation for a wide range of downstream tasks in perioperative medicine. The proposed integrated research and training will be accomplished at Stanford University with the support of an exceptional mentorship team. The candidate will build on a solid background in anesthesiology, biomedical informatics, machine learning, and statistics research to gain relevant expertise in clinical informatics, advanced artificial intelligence methodologies, time-series analysis, causal inference, and scientific communication. Together, the research and training will advance the candidate’s long-term goal to develop an independent research program as a physician-scientist aimed at the development and application of translational artificial intelligence tools that impact perioperative care. Ultimately, the continuum of research will have a positive impact by providing a strong methodological base for targeted applications for improving precision anesthesiology practice.