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 customising 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 (F1:0.448-0.738 vs 0.429-0.650).
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。