用可追溯的自动化系统，从电子病历中提取心脏衰竭特征并提升预测效果。
Tracing the Heart: An Evidence-Linked Pipeline for Heart-Failure Feature Engineering
- 构建多智能体系统，结合指南规则与证据链实现特征自动生成。
- 生成202个结构化特征，使心衰分型模型准确率提升至0.963（HFrEF）。
- 特征具备可审计性，适合临床研究与医疗AI开发人员使用。
电子病历（EHR）特征工程是临床研究和人工智能领域的关键瓶颈，占数据科学家工作量的39-45%。在心衰领域，影响约670万美国成年人，需整合碎片化EHR数据与基于指南的临床推理。现有基于规则或大语言模型（LLM）的方法仅部分自动化，且维护性差、证据溯源能力弱。我们开发了奈比灵多智能体系统（nMAS），一种基于证据链与评分标准的自动化心衰特征工程流水线，在来自九个EHR源表的500条模拟患者记录上评估。nMAS生成132个结构化特征与70个评分聚合特征，经验证具备结构完整性、评分合规性与来源可追溯性，并由受限LLM审计。加入聚合特征后，对射血分数降低型心衰（HFrEF）的外推AUROC从0.895提升至0.963，对射血分数保留型心衰（HFpEF）从0.870升至0.910；独立LLM对证据支持与方法学严谨性的评分达最大分值的81.5%。结果证明复杂心血管EHR数据的自动化、可审计特征工程可行，但评估限于单中心队列，仍需外部验证。
原文摘要 · Abstract (English)
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.
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