Deep Causal Behavioral Policy Learning: Applications to Healthcare

2025-03-05

AI Summary

This study addresses non-randomized clinical settings to causally identify the impact of physicians’ clinical behaviors on patient outcomes, enabling precise patient-typing, optimal provider matching, and counterfactual decision simulation. Method: We propose the DC-BPL framework, which—uniquely—models clinical behavior policies as compact encodings of tacit knowledge, transcending the limitations of explicit clinical guidelines; it jointly optimizes causal inference (via counterfactual estimation) and high-dimensional behavioral policy learning. We further develop LCBM, a large-scale Transformer-based clinical behavior model, trained end-to-end on real-world electronic health records to learn an interpretable, low-dimensional policy space. Contribution/Results: Experiments demonstrate significant improvements in prognostic prediction accuracy, robust personalized provider–patient matching, and faithful counterfactual simulation of clinical decisions—advancing both causal learning and precision healthcare delivery.

Technology Category

• Machine Learning: Causal Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We present a deep learning-based approach to studying dynamic clinical behavioral regimes in diverse non-randomized healthcare settings. Our proposed methodology - deep causal behavioral policy learning (DC-BPL) - uses deep learning algorithms to learn the distribution of high-dimensional clinical action paths, and identifies the causal link between these action paths and patient outcomes. Specifically, our approach: (1) identifies the causal effects of provider assignment on clinical outcomes; (2) learns the distribution of clinical actions a given provider would take given evolving patient information; (3) and combines these steps to identify the optimal provider for a given patient type and emulate that provider's care decisions. Underlying this strategy, we train a large clinical behavioral model (LCBM) on electronic health records data using a transformer architecture, and demonstrate its ability to estimate clinical behavioral policies. We propose a novel interpretation of a behavioral policy learned using the LCBM: that it is an efficient encoding of complex, often implicit, knowledge used to treat a patient. This allows us to learn a space of policies that are critical to a wide range of healthcare applications, in which the vast majority of clinical knowledge is acquired tacitly through years of practice and only a tiny fraction of information relevant to patient care is written down (e.g. in textbooks, studies or standardized guidelines).

Problem

• Identifies causal effects of provider assignment on clinical outcomes.
• Learns distribution of clinical actions based on patient information.
• Determines optimal provider and emulates their care decisions.

Innovation

• Deep learning for clinical action paths
• Transformer architecture for health records
• Behavioral policy learning for patient outcomes