# Stable Longitudinal Screening of Latent Physiological Dysregulation from Psychometric Data Using Machine Learning

## TL;DR

This study shows that machine learning can use psychometric surveys to non-invasively detect hidden physiological dysregulation linked to chronic stress and long-term health.

## Contribution

A novel machine learning pipeline for longitudinal screening of physiological dysregulation using psychometric data, achieving high performance with minimal features.

## Key findings

- Distilled student models outperformed baseline methods with AUC-ROC up to 0.78.
- Predictor dimensionality was reduced by over an order of magnitude without performance loss.
- Longitudinal data improved model training but was not needed for inference.

## Abstract

Physiological dysregulation arising from chronic stress is a key mechanism linking psychosocial factors to long-term health outcomes, yet early identification typically relies on invasive or resource-intensive measurements. This study evaluates whether high-dimensional psychometric survey data can support scalable, non-invasive screening for latent physiological dysregulation. Using longitudinal data from the Midlife in the United States (MIDUS) Waves 2 and 3, we develop a screening-oriented modeling framework that separates longitudinal risk estimation from deployable screening model construction. Physiological targets are defined across inflammatory, metabolic, and neuroendocrine domains using three canonical allostatic load formulations. A teacher–ranking–pruning–student pipeline combines stable feature ranking, parsimony-driven dimensionality reduction, and knowledge distillation. Predictor dimensionality is reduced by more than an order of magnitude without loss of screening performance. Distilled student models consistently outperform linear, tree-based, and direct neural baselines, achieving area under the receiver operating characteristic curve values up to approximately 0.78 and substantial precision–recall lift over baseline prevalence. Longitudinal information is exploited during model development but not required at inference, enabling deployment using psychometric data alone. These findings demonstrate the feasibility of non-invasive screening for latent physiological dysregulation and provide a generalizable framework for translating longitudinal cohort data into deployable population health tools.