心衰数据特征工程又慢又容易出错，nMAS自动管线把HFrEF的AUROC拉到0.963，还能审计特征来源。
电子病历特征工程占数据科学家39%-45%工作量，心衰领域尤其棘手。研究者提出Nimblemind多智能体系统（nMAS），在500份模拟患者记录、9张EHR源表上生成132个结构化特征和70个评分聚合特征。加入聚合特征后，HFrEF分型的held-out AUROC从0.895升至0.963，HFpEF从0.870升至0.910。独立LLM对证据支撑与方法学评分为最高分的81.5%。
Tracing the Heart: An Evidence-Linked Pipeline for Heart-Failure Feature Engineering
Electronic health record (EHR) feature engineering is a major bottleneck in clinical research and AI, accounting for 39-45% of data scientists' workload. This is especially pronounced in heart failure, which affects an estimated 6.7 million U.S. adults and requires integrating fragmented EHR data with disease-specific, guideline-based clinical reasoning. Existing rule-based and large language model (LLM)-based approaches offer only partial automation with limited maintainability and evidence traceability. We developed the Nimblemind Multi-Agent System (nMAS), an evidence-linked, rubric-grounded pipeline for automated heart-failure feature engineering, and evaluated it on 500 dummy patient records from nine EHR source tables. nMAS generated 132 structured and 70 rubric-scored aggregated features, verified for structural integrity, rubric compliance, and provenance, and audited by a restricted LLM. Adding the aggregated features improved held-out AUROC from 0.895 to 0.963 for HFrEF and 0.870 to 0.910 for HFpEF phenotyping, and an independent LLM-based rubric assessment of evidence support and methodological soundness scored the features at 81.5% of maximum points. These results demonstrate the feasibility of automated, auditable feature engineering for complex cardiovascular EHR data, though evaluation was limited to a single-institution cohort and external validation is needed.