CPAgents: Agentic Composite Phenotype Generation for Cardiac Disease Association

Large-scale studies linking heart imaging measurements to disease risk typically rely on pre-defined, single-variable features chosen by experts — an approach that may miss important non-linear relationships or interactions between measurements. CPAgents automates the discovery of richer, composite phenotypes (ratios, polynomial combinations, interaction terms) through a three-agent loop: an Analyst identifies statistical issues, a Proposer generates candidate expressions, and a Verifier validates them against multi-stage criteria. Applied to a large cardiac imaging cohort, the discovered phenotypes outperform baselines across 56 of 72 evaluation combinations spanning nine disease categories. Applications include population-scale cardiovascular risk stratification, imaging biomarker discovery, and automated feature engineering for clinical machine learning. Authors: Zuoou Li, Wenlong Zhao, Kelly Yu, Weitong Zhang, Paul M. Matthews, Wenjia Bai, Bernhard Kainz, Mengyun Qiao Paper: https://arxiv.org/abs/2606.28179v1