Kraljevic, Z;
Searle, T;
Shek, A;
Roguski, L;
Noor, K;
Bean, D;
Mascio, A;
... Dobson, RJB; + view all
(2021)
Multi-domain clinical natural language processing with MedCAT: The Medical Concept Annotation Toolkit.
Artificial Intelligence in Medicine
, 117
, Article 102083. 10.1016/j.artmed.2021.102083.
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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 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 (F1:0.448-0.738 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.
| Type: | Article |
|---|---|
| Title: | Multi-domain clinical natural language processing with MedCAT: The Medical Concept Annotation Toolkit |
| Location: | Netherlands |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.artmed.2021.102083 |
| Publisher version: | http://dx.doi.org/10.1016/j.artmed.2021.102083 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Clinical concept embeddings, Clinical natural language processing, Clinical ontology embeddings, Electronic health record information extraction |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10129853 |
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