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This paper is in the following e-collection/theme issue:

Natural Language Processing (1326) Electronic Health Records (1787) Posttraumatic Stress Disorder (PTSD) (198) Machine Learning (3323)

Published on in Vol 8, No 5 (2020): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15852, first published .
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The Development of the Military Service Identification Tool: Identifying Military Veterans in a Clinical Research Database Using Natural Language Processing and Machine Learning

The Development of the Military Service Identification Tool: Identifying Military Veterans in a Clinical Research Database Using Natural Language Processing and Machine Learning

Authors of this article:

Daniel Leightley1 Author Orcid Image ;   David Pernet1 Author Orcid Image ;   Sumithra Velupillai2, 3 Author Orcid Image ;   Robert J Stewart2, 3 Author Orcid Image ;   Katharine M Mark1 Author Orcid Image ;   Elena Opie1 Author Orcid Image ;   Dominic Murphy1, 4 Author Orcid Image ;   Nicola T Fear1, 5 Author Orcid Image ;   Sharon A M Stevelink1, 6 Author Orcid Image
Article Authors Cited by (7) Tweetations (5) Metrics

Journals

  1. Williamson C, Palmer L, Leightley D, Pernet D, Chandran D, Leal R, Murphy D, Fear N, Stevelink S. Military veterans and civilians’ mental health diagnoses: an analysis of secondary mental health services. Social Psychiatry and Psychiatric Epidemiology 2023;58(7):1029 View
  2. Leightley D, Palmer L, Williamson C, Leal R, Chandran D, Murphy D, Fear N, Stevelink S. Identifying Military Service Status in Electronic Healthcare Records from Psychiatric Secondary Healthcare Services: A Validation Exercise Using the Military Service Identification Tool. Healthcare 2023;11(4):524 View
  3. Serra G, Tomietto M, McGill A, Kiernan M. Developing a nationwide registry of UK veterans seeking help from sector charities—a machine learning approach to stratification. European Journal of Public Health 2025;35(1):5 View
  4. Biscoe N, Leightley D, Murphy D. Developing a Tool for Identifying Clinical Risk From Free-Text Clinical Records: Natural Language Processing Study. JMIR AI 2025;4:e64898 View
  5. Sondh S, Mueller C, Li L, Roberts A, Sondh H, Stewart R. The association between older adult abuse and emergency department attendance and mental health service use. Frontiers in Sociology 2026;11 View

Books/Policy Documents

  1. Leightley D, Sharp M, Williamson V, Fear N, Gribble R. Social Media and the Armed Forces. View
  2. Leightley D, Sharp M, Williamson V, Fear N, Gribble R. Soziale Medien und die Streitkräfte. View