Bending the Learning Curve for EHR Research via Knowledge-Driven Online Multimodal Automated Phenotyping System
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通过知识驱动的在线多模态自动表型系统降低电子健康记录研究门槛
- 研究问题
- 面对诊断编码噪声、优质标签不足及患者数据共享受限，如何利用多机构电子健康记录高效构建并迁移疾病表型算法？
- 方法与思路
- 作者将多来源表示学习、疾病相关概念检索排序和基于汇总统计的表型训练整合为三层系统，同时结合编码与临床文本信息。
- 材料陈述的贡献
- 摘要称，四家医疗中心的验证支持患者分型、表型算法构建和关联研究功效提升，并报告标准方法未发现的遗传变异关联。
- 阅读关注点
- 应核对汇总统计的内容、隐私保护机制、跨机构验证与迁移设计，以及表型准确性和关联研究功效的评估标准。
- 阅读边界
- 仅据所给 OpenAlex 摘要；未说明准确率数值、隐私保证及关联检验校正细节，所述保护性关联不能直接解释为因果效应。
原文摘要
Electronic health records (EHRs) hold great promise for translational research but remain difficult to use at scale because diagnostic codes are noisy, disease-relevant features are hard to identify, high-quality labels are limited and patient-level data sharing is often restricted. We introduce Knowledge-driven Online Multimodal Automated Phenotyping (KOMAP), a unified system for automated, privacy-preserving EHR phenotyping using summary statistics. The system is organized around three original and integrated layers. First, the Multi-source Representation Learning (MultiReL) embedding layer fuses EHR co-occurrence information from multiple health systems with biomedical language-model representations into a shared semantic space. Second, the Online Narrative and Codified feature Engine (ONCE) retrieves and ranks disease-relevant codified and narrative concepts using MultiReL and local EHR support. Third, the KOMAP phenotyping layer trains, validates, and transports disease-specific algorithms from simple summary statistics rather than individual-level patient records. Validation in four healthcare centers demonstrated the ability of KOMAP to support patient subtyping, generate highly accurate phenotyping algorithms, and improve statistical power in downstream phenome-wide association studies (PheWAS). In a PheWAS of a statin-related genetic variant, KOMAP identified significant protective associations for alopecia and seborrheic dermatitis that were not found by standard methods. By combining knowledge-guided feature retrieval with summary-statistics-based phenotyping, the proposed framework reduces privacy, computational, and technical barriers to high-throughput EHR research and supports scalable multi-institutional biomedical discovery.