The Military Service Identification Tool is a software package for classifying medical notes as either belonging to an individual who has or has not served (or is serving) in the military. The Tool was developed by Dr Daniel Leightley and David Pernet from the King's Centre for Military Health Research. The Tool development was funded by the Forces in Mind Trust (Project: FiMT18/0525KCL), a £35 million funding scheme run by the Forces in Mind Trust using an endowment awarded by the National Lottery Community Fund.
It is important to note that this repository is a demonstration and does not contain any data. Due to confidentiality, specific sections have been omitted. Where this is the case, more information has been provided. The Tool will not run. If you'd like to use the Tool in a specific setting please contact Dr Daniel Leightley.
- License
- Publication
- Getting Started
- Availability of Materials and Data
- Other Publications
- Future Work
The Military Service Identification Tool has been released under GNU General Public License (v3) license to promote open source app development and sharing of innovation within the research community. Simply, use freely but make your software accessible to others.
You find out more about the development of the Tool in the following article.
Leightley D, Pernet D, Velupillai S, Stewart RJ, Mark KM, Opie E, Murphy D, Fear NT, Stevelink SA. The Development of the Military Service Identification Tool: Identifying Military Veterans in a Clinical Research Database Using Natural Language Processing and Machine Learning. DOI: [10.2196/15852](https://doi.org/10.2196/15852)
We developed two approaches for the identification of military service, a SQL-based approach and a Python-version. You can navigate to each respective folder to view the source code and explore how each version functions.
The datasets used in this study are based on patient data, which are not publicly available. Although the data are pseudonymized, that is, personal details of the patient are removed, the data still contain information that could be used to identify a patient. Access to these data requires a formal application to the CRIS Patient Data Oversight Committee of the NIHR Biomedical Research Centre. On request and after suitable arrangements are put in place, the data and modelling employed in this study can be viewed within the secure system firewall. The corresponding author can provide more information about the process.
Daniel Leightley, Katharine M. Mark, David Pernet, Dominic Murphy, Sharon A.M. Stevelink and Nicola T. Fear. Identifying veterans using electronic health records in the United Kingdom: A feasibility study. MDPI Healthcare, 2019. [https://doi.org/10.3390/healthcare8010001](https://doi.org/10.3390/healthcare8010001)
We are busy working on a revised Tool that is system agnostic and with a public example. Please subscribe to the repository to be notified when it has been released.