Uncertainty-Calibrated Prediction of Randomly-Timed Biomarker Trajectories with Conformal Bands

TL;DR

This paper introduces a conformal prediction method for uncertainty-calibrated biomarker trajectory forecasts from irregular clinical visits, improving reliability and clinical utility in Alzheimer's disease prediction.

Contribution

It extends conformal prediction to randomly-timed trajectories with a novel nonconformity score, providing guaranteed coverage and enhanced clinical decision-making tools.

Findings

Prediction bands achieve desired coverage levels.

Bands are tighter than baseline methods.

Improved identification of high-risk patients.

Abstract

Despite recent progress in predicting biomarker trajectories from real clinical data, uncertainty in the predictions poses high-stakes risks (e.g., misdiagnosis) that limit their clinical deployment. To enable safe and reliable use of such predictions in healthcare, we introduce a conformal method for uncertainty-calibrated prediction of biomarker trajectories resulting from randomly-timed clinical visits of patients. Our approach extends conformal prediction to the setting of randomly-timed trajectories via a novel nonconformity score that produces prediction bands guaranteed to cover the unknown biomarker trajectories with a user-prescribed probability. We apply our method across a wide range of standard and state-of-the-art predictors for two well-established brain biomarkers of Alzheimer's disease, using neuroimaging data from real clinical studies. We observe that our conformal prediction bands consistently achieve the desired coverage, while also being tighter than baseline prediction bands. To further account for population heterogeneity, we develop group-conditional conformal bands and test their coverage guarantees across various demographic and clinically relevant subpopulations. Moreover, we demonstrate the clinical utility of our conformal bands in identifying subjects at high risk of progression to Alzheimer's disease. Specifically, we introduce an uncertainty-calibrated risk score that enables the identification of 17.5% more high-risk subjects compared to standard risk scores, highlighting the value of uncertainty calibration in real-world clinical decision making. Our code is available at github.com/vatass/ConformalBiomarkerTrajectories.