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

• URL: http://arxiv.org/abs/2010.01165v2
• Date: Thu, 25 Mar 2021 13:21:50 GMT
• Title: Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit
• Authors: Zeljko Kraljevic, Thomas Searle, Anthony Shek, Lukasz Roguski, Kawsar Noor, Daniel Bean, Aurelie Mascio, Leilei Zhu, Amos A Folarin, Angus Roberts, Rebecca Bendayan, Mark P Richardson, Robert Stewart, Anoop D Shah, Wai Keong Wong, Zina Ibrahim, James T Teo, Richard JB Dobson
• Abstract summary: We present the open-source Medical Concept EHR Toolkit (MedMedCAT) It provides a novel self-supervised machine learning algorithm for extracting concepts using any concept vocabulary including UMLS/SNOMED-CT. We show improved performance in extracting UMLS concepts from open datasets. 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.
• Score: 5.49956798378633
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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