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ORIGINAL RESEARCH article
Front. Digit. Health
Sec. Digital Mental Health
Volume 7 - 2025 | doi: 10.3389/fdgth.2025.1544781
Recovery in Personality Disorders: the development and preliminary testing of a novel natural language processing model to identify recovery in mental health electronic records
Provisionally accepted
Giouliana Kadra-Scalzo 1*
Jaya Chaturvedi 1 Oliver Dale 2 Richard D Hayes 1
Lifang Li 1 Shaza Mahmood 1 Jonathan Monk-Cunliffe 3
Angus Roberts 1
Paul Moran 3- 1 King's College London, London, United Kingdom
- 2 Sussex Partnership NHS Foundation Trust, Worthing, England, United Kingdom
- 3 Centre for Academic Mental Health, University of Bristol, Bristol, England, United Kingdom
Introduction The concept of recovery is of great importance in mental health as it emphasises improvements in quality of life and functioning alongside a traditional focus on symptomatic remission. Yet, investigating non-symptomatic recovery in the field of personality disorders has been particularly challenging due to complexities in capturing the occurrence of recovery. Electronic health records (EHRs) provide a robust platform from which episodes of recovery could be detected. However much of the relevant information may be embedded in free text clinical notes, requiring the development of appropriate tools to extract these data.MethodsUsing data from one of Europe’s largest electronic health records databases (the Clinical Records Interactive Search (CRIS)), we developed and evaluated natural language processing (NLP) models for the identification of occupational and activities of daily living (ADL) recovery among individuals diagnosed with personality disorder. Results The models on ADL performed better (precision: 0.80; 95%CI: 0.73-0.84) than those on occupational recovery (precision: 0.62; 95%CI: 0.52-0.72). However, the models performed less acceptably in correctly identifying all those who recovered, generally missing at least 50% of the population of those who had recovered. Conclusion It is feasible to develop NLP models for the identification of recovery domains for individuals with a diagnosis of personality disorder. Future research needs to improve the efficiency of pre-processing strategies to handle long clinical documents.
Keywords: personality disorder, Recovery, Electronic Health Records, Work, Mental Health, Natural Language Processing
Received: 13 Dec 2024; Accepted: 17 Mar 2025.
Copyright: © 2025 Kadra-Scalzo, Chaturvedi, Dale, Hayes, Li, Mahmood, Monk-Cunliffe, Roberts and Moran. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Giouliana Kadra-Scalzo, King's College London, London, United Kingdom
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