Non-parametric Evaluation of Contextually Optimal Decision-Aids

Music City Center 

The growth of AI- and ML-based clinical decision tools provides an array of decision-aid agents that can be implemented into a clinician's decision-making process. However few tools exist for context-specific evaluation of the alignment of these agents with clinicians' workflows, and thus no method to identify an optimal set of aligned agents to adopt. Our work adopts the multinomial logit choice (MNL) model as a framework for evaluating agent-alignment and identifying an optimal agent-set. We assume the observation of selections among a set of agents according to a context-dependent MNL model, characterized by context-dependent preference parameters. We propose a standard regularized maximum likelihood estimation (MLE) procedure, providing a uniform convergence rate over a bounded context space. Additionally, when agent-specific utility parameters or functions are known, an optimal assortment of agents can be identified. This work novelly estimates context-specific alignment of decision-making agents, using results in relevance-weighted likelihood, uniform rates in non-parametric kernel regression, and previous results from the static MNL model.

decision-aids

non-parametric regression

relevance-weighted likelihood

optimal assortment 

ENAR