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

• Published in: JMIR medical informatics Vol. 8; no. 5; p. e15852
• Main Authors: Leightley, Daniel, Pernet, David, Velupillai, Sumithra, Stewart, Robert J, Mark, Katharine M, Opie, Elena, Murphy, Dominic, Fear, Nicola T, Stevelink, Sharon A M
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 25-05-2020
• Subjects: Archives & records; Armed forces; Datasets; Decision making; Identification; Machine learning; Medical research; Mental health; Military service; Natural language processing; Original Paper; Patients; Performance evaluation; Structured Query Language-SQL; Veterans; England; Scotland; United Kingdom > UK; Wales; electronic health care records; natural language processing; veteran; machine learning; mental health; military personnel
• ISSN: 2291-9694; 2291-9694
• DOI: 10.2196/15852

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