Authors: Jonas Knecht, Anna Zink, Jonathan Kolstad, Maya Petersen
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).
Source: http://arxiv.org/abs/2503.03724v1
