Machine Learning in Health Research

Ensemble methods, mixture models, and semiparametric estimation for clinical and neuroimaging data

2026-06-07 18:16 PDT

Overview

Machine learning methods offer predictive capacity that classical regression models cannot achieve when covariate spaces are high-dimensional, outcome distributions are complex, or the functional form of the association is unknown. In clinical research, however, the standards for model transparency, stability across sites, and interpretability are demanding: a model that predicts well on training data but cannot be explained to a clinician, replicated in a new cohort, or used to inform a treatment decision has limited scientific value.

This program applies and evaluates machine learning methods in clinical settings where those standards can be met – principally, ensemble methods for binary clinical outcomes with large multi-cohort validation, mixture models for MRI-derived biomarkers, and semiparametric density estimation for neuroimaging data analysis.

Papers in development

  • Concomitant medication exposure as a predictor of MCI-to-AD conversion: ensemble machine learning analysis across three multi-site cohorts.

  • Machine learning applied to ADNI neuroimaging and clinical data for disease staging and progression prediction.

  • Normal mixture models for MRI-derived biomarker distributions in Alzheimer’s disease.

  • Evaluation of random-forest imputation for high-dimensional, mixed-type missingness in multi-site AD cohort studies.

Methods

Gradient boosting (xgboost, gbm); random forests (randomForest, ranger); LASSO via glmnet; normal mixture models via mclust; empirical characteristic function estimation; Fourier-based nonparametric density estimation; multiple imputation via mice and missForest; cross-validated performance evaluation (AUROC, Brier score) with site-stratified folds.

Software

  • zzlongplot – Visualization of longitudinal prediction trajectories and model performance across cohorts.
  • zztable1 – Descriptive tables for multi-site machine learning study populations.

Publications

The full publications list filtered by machine-learning or predictive-modeling provides the relevant publication record, which includes early neural-network and classification work from the 1990s and the current ensemble ML program.