这篇论文提出了CPAgents，用三个智能体自动组合心脏影像特征，相比传统方法在56/72测试中拿第一。适合关心AI辅助医学研究的读者。
CPAgents是一个由三个AI智能体（Analyst、Proposer、Verifier）组成的迭代框架，用于自动构建心血管表型关联研究中的可解释复合表型（如多项式、比值、交互形式）。在72个分类器-疾病-指标组合中，CPAgents生成的复合表型在56个组合中取得最优排名（基线仅18个），涵盖全部9个临床疾病类别。该方法能自动发现超出专家手工特征选择的更强表型-疾病关联，并生成透明的证据链。
CPAgents: Agentic Composite Phenotype Generation for Cardiac Disease Association
Identifying robust associations between cardiac imaging phenotypes and clinical diseases is fundamental to population-scale cardiovascular research and reliable risk stratification. However, current phenome-wide association studies rely on pre-defined, single-variable phenotypes or expert-crafted features, which limits their ability to capture clinically meaningful non-linear effects and cross-phenotype interactions. To address this, we propose CPAgents, an iterative phenotype-Composition framework for cardiovascular Phenome-wide association study (PheWAS) that automatically constructs and validates interpretable composite phenotypes (e.g., polynomial, ratio, and interaction forms) from base imaging features. Specifically, our system coordinates three agents: (i) an Analyst that identifies statistical pathologies and nominates candidate transformations; (ii) a Proposer that generates constrained, medically and statistically motivated expressions under numerical safety rules; and (iii) a Verifier that evaluates candidates using multi-stage criteria and produces transparent evidence trails for accepted phenotypes. Evaluated on a population-scale cardiac imaging cohort, the discovered composite phenotypes markedly improve disease discrimination: across 72 classifier-disease-metric combinations, our variants achieve the top rank in 56 cases versus 18 for baselines, with gains observed across all nine clinical disease categories. Our framework yields compact, clinically interpretable phenotype formulas with transparent evidence trails, enabling scalable discovery of stronger phenotype-disease associations beyond expert-driven feature selection.