Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of Information Extraction (IE) technologies to enable clinical analysis. We present the open source Medical Concept Annotation Toolkit (MedCAT) that provides: a) a novel self-supervised machine learning algorithm for extracting concepts using any concept vocabulary including UMLS/SNOMED-CT; b) a feature-rich annotation interface for customizing and training IE models; and c) integrations to the broader CogStack ecosystem for vendor-agnostic health system deployment. We show improved performance in extracting UMLS concepts from open datasets ( F1 0.467-0.791 vs 0.384-0.691). Further real-world validation demonstrates SNOMED-CT extraction at 3 large London hospitals with self-supervised training over ~8.8B words from ~17M clinical records and further fine-tuning with ~6K clinician annotated examples. We show strong transferability ( F1 >0.94) between hospitals, datasets and concept types indicating cross-domain EHR-agnostic utility for accelerated clinical and research use cases.

介绍了一种开源的医学概念注释工具(MedCAT)，该工具使用自我监督的机器学习算法从电子医疗记录中提取UMLS/SNOMED-CT概念，从而实现了对大量结构化文本的临床分析。该工具包括：自定义和训练IE模型的功能丰富的注释界面;以及与CogStack生态系统的集成用于厂商无关的卫生系统部署。现实世界的验证表明，该工具在大型伦敦医院的SNOMED-CT提取方面的性能表现很好，表明了跨领域的EHR-agnostic实用性，可加速临床和研究用例。

MedCAT多领域临床自然语言处理：医学概念标注工具包