POMA-C: A Framework for Solving the Problem of Precise Anesthesia Control Under Incomplete Observation Environment in Low-Income Areas

This paper introduces the POMA-C (Partial Observable Model for Anesthesia Control) framework, developed to address the challenge of anesthesia management in environments with incomplete physiological monitoring, such as low-resource settings where critical indicators like the Bispectral Index (BIS) are often unavailable. Unlike traditional methods that rely on fully observable data, POMA-C frames the problem of anesthesia control under incomplete observability within a Partially Observable Markov Decision Process (POMDP), enabling the precise control of anesthesia despite missing data. By establishing a formal correspondence between the anesthesia control process and POMDP, this framework provides a theoretical foundation for modeling anesthesia control in uncertain environments. The framework employs the POMCPOW (Partially Observable Monte Carlo Planning with Observation Weighting) algorithm, which integrates Monte Carlo Tree Search (MCTS) and particle filtering to estimate the patient’s true physiological state and guide optimal anesthetic decisions. Through comprehensive ablation experiments—where key observation dimensions are systematically reduced to simulate missing data—POMA-C demonstrates significantly higher decision accuracy and cumulative reward optimization compared to methods like Q-learning and human expertise, even in data-constrained environments. This work not only provides a robust solution for anesthesia control under incomplete observability but also bridges the gap between MDP and POMDP models, offering a foundation for future research in automated anesthesia management.