Expert-Guided POMDP Learning for Data-Efficient Modeling in Healthcare

2025-11-18

AI Summary

Parameter learning for Partially Observable Markov Decision Processes (POMDPs) in healthcare is severely hindered by data sparsity and high noise, limiting model reliability and interpretability. Method: This paper proposes Fuzzy MAP EM—a novel Expectation-Maximization (EM) algorithm that encodes domain expert knowledge as fuzzy rules and translates them into pseudo-counts, thereby embedding structured prior knowledge directly into the EM framework for maximum a posteriori (MAP) estimation. Contribution/Results: The method eliminates reliance on large-scale labeled datasets and significantly enhances robustness and interpretability of parameter estimation under low-sample and high-noise conditions. On synthetic medical benchmarks, Fuzzy MAP EM achieves faster convergence and higher accuracy than standard EM. Applied to real-world myasthenia gravis cases, it successfully induces a clinically plausible disease progression model, empirically validating both its efficacy and practical utility in clinical decision support.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Probabilistic Circuits and Graphical Models
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Learning the parameters of Partially Observable Markov Decision Processes (POMDPs) from limited data is a significant challenge. We introduce the Fuzzy MAP EM algorithm, a novel approach that incorporates expert knowledge into the parameter estimation process by enriching the Expectation Maximization (EM) framework with fuzzy pseudo-counts derived from an expert-defined fuzzy model. This integration naturally reformulates the problem as a Maximum A Posteriori (MAP) estimation, effectively guiding learning in environments with limited data. In synthetic medical simulations, our method consistently outperforms the standard EM algorithm under both low-data and high-noise conditions. Furthermore, a case study on Myasthenia Gravis illustrates the ability of the Fuzzy MAP EM algorithm to recover a clinically coherent POMDP, demonstrating its potential as a practical tool for data-efficient modeling in healthcare.

Problem

• Learning POMDP parameters from limited healthcare data efficiently
• Incorporating expert knowledge into parameter estimation using fuzzy models
• Improving learning performance under low-data and high-noise conditions

Innovation

• Fuzzy MAP EM algorithm integrates expert knowledge
• Uses fuzzy pseudo-counts for parameter estimation
• Reformulates learning as Maximum A Posteriori estimation