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Abstract Computational methods from reinforcement learning have shown promise in inferring treatment strategies for hypotension management and other clinical decision-making challenges. Unfortunately, the resulting models are often difficult for clinicians to interpret, making clinical inspection and validation of these computationally derived strategies challenging in advance of deployment. In this work, we develop a general framework for identifying succinct sets of clinical contexts in which clinicians make very different treatment choices, tracing the effects of those choices, and inferring a set of recommendations for those specific contexts. By focusing on these few key decision points, our framework produces succinct, interpretable treatment strategies that can each be easily visualized and verified by clinical experts. This interrogation process allows clinicians to leverage the model’s use of historical data in tandem with their own expertise to determine which recommendations are worth investigating further e.g. at the bedside. We demonstrate the value of this approach via application to hypotension management in the ICU, an area with critical implications for patient outcomes that lacks data-driven individualized treatment strategies; that said, our framework has broad implications on how to use computational methods to assist with decision-making challenges on a wide range of clinical domains.
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A parameter recovery assessment of a wide class of evidence accumulation models of decision-making.
Computational modeling has become indispensable in investigating the dynamics of decision making processes. A prominent category of models in this domain are Evidence Accumulation Models (EAMs), which model both the decisions people make and the time they take. Many variations have been proposed which modify the drift rate, diffusion rate, and decision thresholds, encoding increasingly complex dynamics into the EAM framework. However, adding model features complicates parameter recovery, making model interpretation more difficult. In this work, we perform a parameter recovery study to a variety of common binary choice EAMs, identify the specific challenges for each, and explore how to improve their parameter recoverability. Though previous studies have addressed this question, they have been piecemeal in nature, with different groups applying different computational methods to study different models. We aim to unify this body of literature using the best currently available computational methods. Further, we present the first, to our knowledge, Bayesian analysis of diffusion conflict models. Our purpose here is to be thorough, not exhaustive or comprehensive. With this in mind, this article catalogues a number of results, some previously shown and some new. Further, it illustrates different approaches to model analysis. This article is intended to be a resource for researchers interested in utilizing EAMs for studying decision-making processes, providing insights into the challenges associated these models, how to analyze them in light of those challenges, and examples of how to address those challenges.
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- Award ID(s):
- 2242962
- PAR ID:
- 10571081
- Publisher / Repository:
- OSF
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Posted Content:
- https://doi.org/10.31234/osf.io/3ca6y_v1
