Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection

TL;DR

This paper investigates how label selection biases affect performance estimates of clinical machine learning models and proposes methods to correct these biases for more accurate evaluation.

Contribution

It introduces a framework for understanding label selection bias, evaluates its impact through simulations, and proposes a combined randomization and weighting approach to recover true model performance.

Findings

Naive performance estimates can be significantly biased by label selection mechanisms.

Properly specified weighting estimators can recover true performance metrics.

A combined randomization and weighting deployment procedure improves performance estimation accuracy.

Abstract

When evaluating the performance of clinical machine learning models, one must consider the deployment population. When the population of patients with observed labels is only a subset of the deployment population (label selection), standard model performance estimates on the observed population may be misleading. In this study we describe three classes of label selection and simulate five causally distinct scenarios to assess how particular selection mechanisms bias a suite of commonly reported binary machine learning model performance metrics. Simulations reveal that when selection is affected by observed features, naive estimates of model discrimination may be misleading. When selection is affected by labels, naive estimates of calibration fail to reflect reality. We borrow traditional weighting estimators from causal inference literature and find that when selection probabilities are properly specified, they recover full population estimates. We then tackle the real-world task of monitoring the performance of deployed machine learning models whose interactions with clinicians feed-back and affect the selection mechanism of the labels. We train three machine learning models to flag low-yield laboratory diagnostics, and simulate their intended consequence of reducing wasteful laboratory utilization. We find that naive estimates of AUROC on the observed population undershoot actual performance by up to 20%. Such a disparity could be large enough to lead to the wrongful termination of a successful clinical decision support tool. We propose an altered deployment procedure, one that combines injected randomization with traditional weighted estimates, and find it recovers true model performance.