Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit

2020/10/02 by Zeljko Kraljevic, Thomas Searle, Kraljevic, Zeljko +33 · 15 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #cs.AI #cs.CL #cs.LG

paper · pdf · doi:10.48550/arxiv.2010.01165

Preprint: 27 Pages, 3 Figures

arxiv created 2021/03/25 · arxiv updated 2021/03/26

Abstract

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.

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Related

• MedCAT -- Medical Concept Annotation Tool
• A Concept Annotation System for Clinical Records
• Clinical Concept Extraction with Contextual Word Embedding
• Text2Node: a Cross-Domain System for Mapping Arbitrary Phrases to a Taxonomy
• Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data