Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-Automated Simulation Based on the LeoPARDS Trial

Hegler C. Tissot; Anoop D. Shah; David Brealey; Steve Harris; Ruth Agbakoba; Amos Folarin; Luis Romao; Lukasz Roguski; Richard Dobson; Folkert W. Asselbergs

doi:10.1109/JBHI.2020.2977925

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Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-Automated Simulation Based on the LeoPARDS Trial

Journal article

Open access

Peer reviewed

Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-Automated Simulation Based on the LeoPARDS Trial

Hegler C. Tissot, Anoop D. Shah, David Brealey, Steve Harris, Ruth Agbakoba, Amos Folarin, Luis Romao, Lukasz Roguski, Richard Dobson and Folkert W. Asselbergs

IEEE journal of biomedical and health informatics, v 24(10), pp 2950-2959

01 Oct 2020

DOI: https://doi.org/10.1109/JBHI.2020.2977925

PMID: 32149659

Featured in Collection : UN Sustainable Development Goals @ Drexel

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Abstract

Clinical trials

Electric shock

electronic medical records

Health information management

Informatics

Medical diagnostic imaging

Natural language processing

patient monitoring

Recruitment

text processing

Unified modeling language

Clinical trials often fail to recruit an adequate number of appropriate patients. Identifying eligible trial participants is resource-intensive when relying on manual review of clinical notes, particularly in critical care settings where the time window is short. Automated review of electronic health records (EHR) may help, but much of the information is in free text rather than a computable form. We applied natural language processing (NLP) to free text EHR data using the CogStack platform to simulate recruitment into the LeoPARDS study, a clinical trial aiming to reduce organ dysfunction in septic shock. We applied an algorithm to identify eligible patients using a moving 1-hour time window, and compared patients identified by our approach with those actually screened and recruited for the trial, for the time period that data were available. We manually reviewed records of a random sample of patients identified by the algorithm but not screened in the original trial. Our method identified 376 patients, including 34 patients with EHR data available who were actually recruited to LeoPARDS in our centre. The sensitivity of CogStack for identifying patients screened was 90% (95% CI 85%, 93%). Of the 203 patients identified by both manual screening and CogStack, the index date matched in 95 (47%) and CogStack was earlier in 94 (47%). In conclusion, analysis of EHR data using NLP could effectively replicate recruitment in a critical care trial, and identify some eligible patients at an earlier stage, potentially improving trial recruitment if implemented in real time.

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36 citations in Web of Science

44 citations in Scopus

Details

Title: Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-Automated Simulation Based on the LeoPARDS Trial
Creators: Hegler C. Tissot - University College London
Anoop D. Shah - University College London
David Brealey - University College London
Steve Harris - University College London
Ruth Agbakoba - University College London
Amos Folarin - University College London
Luis Romao - University College London
Lukasz Roguski - Institute of Health Informatics, University College London, London, U.K
Richard Dobson - University College London
Folkert W. Asselbergs - University College London
Publication Details: IEEE journal of biomedical and health informatics, v 24(10), pp 2950-2959
Publisher: IEEE
Grant note: UK Medical Research Council Division of Critical Care postdoctoral fellowship UCL Hospitals NIHR Biomedical Research Centre National Institute for Health Research (10.13039/501100000272) European Unions Horizon 2020 NIHR University College London Hospitals Biomedical Research Centre 116074 / Innovative Medicines Initiative (10.13039/501100010767) Health and Social Care Research and Development Division (10.13039/501100010756) University College Hospital EFPIA The BigData@Heart Consortium Public Health Agency (10.13039/501100001626) National Institute for Health Research University College London Hospitals Biomedical Research Centre Chief Scientist Office of the Scottish Government Health and Social Care Directorates NIHR Biomedical Research Centre at South London South London and Maudsley NHS Foundation Trust (10.13039/100009362) Institute of Health Informatics at University College London LOND1 / Health Data Research UK Engineering and Physical Sciences Research Council (10.13039/501100000266) Economic and Social Research Council (10.13039/501100000269) Health Research University College London Hospitals Biomedical Research Centre Department of Health and Social Care Public Health Agency (Northern Ireland) Kings College London (10.13039/501100000764) NIHR Health Informatics Collaborative British Heart Foundation (10.13039/501100000274) Health Data Research UK
Resource Type: Journal article
Language: English
Academic Unit: Information Science
Web of Science ID: WOS:000576429900024
Scopus ID: 2-s2.0-85092750251
Other Identifier: 991021861660504721

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Collaboration types: Domestic collaboration; International collaboration
Web of Science research areas: Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics