TY - JOUR
T1 - Multi-domain clinical natural language processing with MedCAT
T2 - The Medical Concept Annotation Toolkit
AU - Kraljevic, Zeljko
AU - Searle, Thomas
AU - Shek, Anthony
AU - Roguski, Lukasz
AU - Noor, Kawsar
AU - Bean, Daniel
AU - Mascio, Aurelie
AU - Zhu, Leilei
AU - Folarin, Amos A.
AU - Roberts, Angus
AU - Bendayan, Rebecca
AU - Richardson, Mark P.
AU - Stewart, Robert
AU - Shah, Anoop D.
AU - Wong, Wai Keong
AU - Ibrahim, Zina
AU - Teo, James T.
AU - Dobson, Richard J. B.
N1 - Funding Information:
We would like to thank all the clinicians who provided annotation training for MedAT; this includes Rosita Zakeri, Kevin O'Gallagher, Rosemary Barker, David Nicholson Thomas, Rhian Raftopoulos, Pedro Viana, Elisa Bruno, Eugenio Abela, Mark Richardson, Naoko Skiada, Luwaiza Mirza, Natalia Chance, Jaya Chaturvedi, Tao Wang, Matt Solomon, Charlotte Ramsey and James Teo. RD's work is supported by (1) National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. (2) Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome Trust. (3) The National Institute for Health Research University College London Hospitals Biomedical Research Centre. DMB is funded by a UKRI Innovation Fellowship as part of Health Data Research UK MR/S00310X/1 (https://www.hdruk.ac.uk). RB is funded in part by grant MR/R016372/1 for the King's College London MRC Skills Development Fellowship programme funded by the UK Medical Research Council (MRC, https://mrc.ukri.org) and by grant IS-BRC-1215-20018 for the National Institute for Health Research (NIHR, https://www.nihr.ac.uk) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. ADS is supported by a postdoctoral fellowship from THIS Institute. AS is supported by a King's Medical Research Trust studentship. RS is part-funded by: (i) the National Institute for Health Research (NIHR) Biomedical Research Centre at the South London and Maudsley NHS Foundation Trust and King's College London; (ii) a Medical Research Council (MRC) Mental Health Data Pathfinder Award to King's College London; (iii) an NIHR Senior Investigator Award; (iv) the National Institute for Health Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King's College Hospital NHS Foundation Trust. This paper represents independent research part funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, The UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare (AI4VBH); the National Institute for Health Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) and King's College London. The views expressed are those of the author(s) and not necessarily those of the NHS, MRC, NIHR or the Department of Health and Social Care. We thank the patient experts of the KERRI committee, Professor Irene Higginson, Professor Alastair Baker, Professor Jules Wendon, Professor Ajay Shah, Dan Persson and Damian Lewsley for their support.
Funding Information:
JTHT received research support and funding from InnovateUK, Bristol-Myers-Squibb, iRhythm Technologies, and holds shares 5000 in Glaxo Smithkline and Biogen.
Funding Information:
ADS is supported by a postdoctoral fellowship from THIS Institute. AS is supported by a King's Medical Research Trust studentship. RS is part-funded by: (i) the National Institute for Health Research (NIHR) Biomedical Research Centre at the South London and Maudsley NHS Foundation Trust and King's College London; (ii) a Medical Research Council (MRC) Mental Health Data Pathfinder Award to King's College London; (iii) an NIHR Senior Investigator Award; (iv) the National Institute for Health Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King's College Hospital NHS Foundation Trust.
Funding Information:
This paper represents independent research part funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust , The UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare (AI4VBH) ; the National Institute for Health Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) and King's College London . The views expressed are those of the author(s) and not necessarily those of the NHS, MRC, NIHR or the Department of Health and Social Care. We thank the patient experts of the KERRI committee, Professor Irene Higginson, Professor Alastair Baker, Professor Jules Wendon, Professor Ajay Shah, Dan Persson and Damian Lewsley for their support.
Funding Information:
RB is funded in part by grant MR/R016372/1 for the King's College London MRC Skills Development Fellowship programme funded by the UK Medical Research Council (MRC, https://mrc.ukri.org ) and by grant IS-BRC-1215-20018 for the National Institute for Health Research (NIHR, https://www.nihr.ac.uk ) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London.
Funding Information:
RD's work is supported by (1) National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. (2) Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome Trust. (3) The National Institute for Health Research University College London Hospitals Biomedical Research Centre.
Funding Information:
DMB is funded by a UKRI Innovation Fellowship as part of Health Data Research UK MR/S00310X/1 ( https://www.hdruk.ac.uk ).
Publisher Copyright:
© 2021 Elsevier B.V.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/7
Y1 - 2021/7
N2 - 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.
AB - 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.
KW - Clinical concept embeddings
KW - Clinical natural language processing
KW - Clinical ontology embeddings
KW - Electronic health record information extraction
UR - http://www.scopus.com/inward/record.url?scp=85106551455&partnerID=8YFLogxK
U2 - 10.1016/j.artmed.2021.102083
DO - 10.1016/j.artmed.2021.102083
M3 - Article
AN - SCOPUS:85106551455
SN - 0933-3657
VL - 117
JO - Artificial Intelligence in Medicine
JF - Artificial Intelligence in Medicine
M1 - 102083
ER -