Interpretable Machine Learning for Resource Allocation with Application to Ventilator Triage
Julien Grand-Cl\'ement, You Hui Goh, Carri Chan, Vineet Goyal,, Elizabeth Chuang

TL;DR
This paper introduces an interpretable, data-driven model for resource allocation during crises, specifically creating simple decision tree-based triage guidelines for ventilator allocation in COVID-19, improving outcomes over existing protocols.
Contribution
The paper develops a novel algorithm for optimal tree policies in Markov Decision Processes, applying it to create transparent triage guidelines for ventilator allocation during pandemics.
Findings
Reduced excess deaths with the new guidelines
Identified limitations of existing triage protocols
Demonstrated the effectiveness of interpretable decision trees
Abstract
Rationing of healthcare resources is a challenging decision that policy makers and providers may be forced to make during a pandemic, natural disaster, or mass casualty event. Well-defined guidelines to triage scarce life-saving resources must be designed to promote transparency, trust, and consistency. To facilitate buy-in and use during high-stress situations, these guidelines need to be interpretable and operational. We propose a novel data-driven model to compute interpretable triage guidelines based on policies for Markov Decision Process that can be represented as simple sequences of decision trees ("tree policies"). In particular, we characterize the properties of optimal tree policies and present an algorithm based on dynamic programming recursions to compute good tree policies. We utilize this methodology to obtain simple, novel triage guidelines for ventilator allocations for…
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Taxonomy
TopicsEmergency and Acute Care Studies · Disaster Response and Management · Disaster Management and Resilience
