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

Daniel Leightley; David Pernet; Sumithra Velupillai; Robert J Stewart; Katharine M Mark; Elena Opie; Dominic Murphy; Nicola T Fear; Sharon A M Stevelink

doi:10.2196/15852

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

JMIR Med Inform. 2020 May 25;8(5):e15852. doi: 10.2196/15852.

Authors

Daniel Leightley¹, David Pernet¹, Sumithra Velupillai^{2

3}, Robert J Stewart^{2

3}, Katharine M Mark¹, Elena Opie¹, Dominic Murphy^{1

4}, Nicola T Fear^{1

5}, Sharon A M Stevelink^{1

6}

Affiliations

¹ King's Centre for Military Health Research, King's College London, London, United Kingdom.
² Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
³ South London and Maudsley NHS Foundation Trust, London, United Kingdom.
⁴ Combat Stress, Letherhead, United Kingdom.
⁵ Academic Department of Military Mental Health, King's College London, London, United Kingdom.
⁶ Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

PMID: 32348287
PMCID: PMC7281146
DOI: 10.2196/15852

Abstract

Background: Electronic health care records (EHRs) are a rich source of health-related information, with potential for secondary research use. In the United Kingdom, there is no national marker for identifying those who have previously served in the Armed Forces, making analysis of the health and well-being of veterans using EHRs difficult.

Objective: This study aimed to develop a tool to identify veterans from free-text clinical documents recorded in a psychiatric EHR database.

Methods: Veterans were manually identified using the South London and Maudsley (SLaM) Biomedical Research Centre Clinical Record Interactive Search-a database holding secondary mental health care electronic records for the SLaM National Health Service Foundation Trust. An iterative approach was taken; first, a structured query language (SQL) method was developed, which was then refined using natural language processing and machine learning to create the Military Service Identification Tool (MSIT) to identify if a patient was a civilian or veteran. Performance, defined as correct classification of veterans compared with incorrect classification, was measured using positive predictive value, negative predictive value, sensitivity, F1 score, and accuracy (otherwise termed Youden Index).

Results: A gold standard dataset of 6672 free-text clinical documents was manually annotated by human coders. Of these documents, 66.00% (4470/6672) were then used to train the SQL and MSIT approaches and 34.00% (2202/6672) were used for testing the approaches. To develop the MSIT, an iterative 2-stage approach was undertaken. In the first stage, an SQL method was developed to identify veterans using a keyword rule-based approach. This approach obtained an accuracy of 0.93 in correctly predicting civilians and veterans, a positive predictive value of 0.81, a sensitivity of 0.75, and a negative predictive value of 0.95. This method informed the second stage, which was the development of the MSIT using machine learning, which, when tested, obtained an accuracy of 0.97, a positive predictive value of 0.90, a sensitivity of 0.91, and a negative predictive value of 0.98.

Conclusions: The MSIT has the potential to be used in identifying veterans in the United Kingdom from free-text clinical documents, providing new and unique insights into the health and well-being of this population and their use of mental health care services.

Keywords: electronic health care records; machine learning; mental health; military personnel; natural language processing; veteran.

©Daniel Leightley, David Pernet, Sumithra Velupillai, Robert J Stewart, Katharine M Mark, Elena Opie, Dominic Murphy, Nicola T Fear, Sharon A M Stevelink. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 25.05.2020.