Reducing overconfident errors in clinical prediction models.
TL;DR
The results of this paper suggest that taming a model's tendency to predict overconfidently using historical error patterns may be a critical step towards safer and more reliable ML systems for digital public health.
Abstract
Machine Learning (ML) models are increasingly being used in clinical workflows. Evaluation of these models tends to focus on global performance metrics, which can obscure error patterns and lead to bias in clinical decision making. Here, we propose Proximal Error-Based Confidence Adjustment (PECA), a framework designed explicitly to improve the safety of ML predictions by reducing a model's confidence in regions of the feature space associated with historical prediction errors. Rather than optimizing global metrics alone, PECA targets confident misclassifications by attenuating prediction confidence proportional to similarity with previously observed errors. Across extensive simulations, PECA reduced the rate at which a model made confident misclassifications while preserving the overall predictive ability of the model. We applied this framework to a clinical trial enrollment workflow for Alzheimer's disease and demonstrated consistent results with the simulations while also demonstrating superior statistical power compared to baseline models. The results of this paper suggest that taming a model's tendency to predict overconfidently using historical error patterns may be a critical step towards safer and more reliable ML systems for digital public health.