Abstract
Digital twins -- virtual replicas of physical entities -- are gaining traction in healthcare for personalized monitoring, predictive modeling, and clinical decision support. However, generating interoperable patient digital twins from unstructured electronic health records (EHRs) remains challenging due to variability in clinical documentation and lack of standardized mappings. This paper presents a semantic NLP-driven pipeline that transforms free-text EHR notes into FHIR-compliant digital twin representations. The pipeline leverages named entity recognition (NER) to extract clinical concepts, concept normalization to map entities to SNOMED-CT or ICD-10, and relation extraction to capture structured associations between conditions, medications, and observations. Evaluation on MIMIC-IV Clinical Database Demo with validation against MIMIC-IV-on-FHIR reference mappings demonstrates high F1-scores for entity and relation extraction, with improved schema completeness and interoperability compared to baseline methods.
Abstract (translated)
数字孪生——物理实体的虚拟副本——在医疗保健领域用于个性化监测、预测建模和临床决策支持方面正逐渐受到重视。然而，从非结构化的电子健康记录（EHRs）生成可互操作的患者数字孪生仍然面临挑战，主要是由于临床文档的变异性以及缺乏标准化映射所致。本文提出了一种基于语义自然语言处理（NLP）的管道，该管道将自由文本形式的EHR笔记转换为符合FHIR标准的数字孪生表示。此管道利用命名实体识别（NER）来提取临床概念、概念规范化以将实体映射到SNOMED-CT或ICD-10，并通过关系抽取捕捉条件、药物和观察结果之间的结构化关联。 使用MIMIC-IV临床数据库Demo进行评估，对照组为MIMIC-IV-on-FHIR参考映射验证表明，在实体和关系提取方面达到了高F1分数。与基线方法相比，此管道提高了模式完整性和互操作性。
URL
https://arxiv.org/abs/2601.05847
