| Literature DB >> 34127232 |
Zeljko Kraljevic1, Thomas Searle2, Anthony Shek3, Lukasz Roguski4, Kawsar Noor4, Daniel Bean5, Aurelie Mascio2, Leilei Zhu6, Amos A Folarin7, Angus Roberts8, Rebecca Bendayan2, Mark P Richardson3, Robert Stewart9, Anoop D Shah4, Wai Keong Wong6, Zina Ibrahim1, James T Teo10, Richard J B Dobson11.
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.Entities:
Keywords: Clinical concept embeddings; Clinical natural language processing; Clinical ontology embeddings; Electronic health record information extraction
Mesh：
Year: 2021 PMID： 34127232 DOI： 10.1016/j.artmed.2021.102083
Source DB: PubMed Journal: Artif Intell Med ISSN： 0933-3657 Impact factor: 5.326