Bending the Learning Curve for EHR Research via Knowledge-Driven Online Multimodal Automated Phenotyping System
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
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…