Projects per year
Abstract
Introduction: Mood instability is an important problem but has received relatively little research attention. Natural language processing (NLP) is a novel method, which can used to automatically extract clinical data from electronic health records (EHRs).
Aims To extract mood instability data from EHRs and investigate its impact on people with mental health disorders.
Methods: Data on mood instability were extracted using NLP from 27,704 adults receiving care from the South London and Maudsley NHS Foundation Trust (SLaM) for affective, personality or psychotic disorders. These data were used to investigate the association of mood instability with different mental disorders and with hospitalisation and treatment outcomes.
Results: Mood instability was documented in 12.1% of people included in the study. It was most frequently documented in people with bipolar disorder (22.6%), but was also common in personality disorder (17.8%) and schizophrenia (15.5%). It was associated with a greater number of days spent in hospital (B coefficient 18.5, 95% CI 12.1–24.8), greater frequency of hospitalisation (incidence rate ratio 1.95, 1.75–2.17), and an increased likelihood of prescription of antipsychotics (2.03, 1.75–2.35).
Conclusions: Using NLP, it was possible to identify mood instability in a large number of people, which would otherwise not have been possible by manually reading clinical records. Mood instability occurs in a wide range of mental disorders. It is generally associated with poor clinical outcomes. These findings suggest that clinicians should screen for mood instability across all common mental health disorders. The data also highlight the utility of NLP for clinical research.
Aims To extract mood instability data from EHRs and investigate its impact on people with mental health disorders.
Methods: Data on mood instability were extracted using NLP from 27,704 adults receiving care from the South London and Maudsley NHS Foundation Trust (SLaM) for affective, personality or psychotic disorders. These data were used to investigate the association of mood instability with different mental disorders and with hospitalisation and treatment outcomes.
Results: Mood instability was documented in 12.1% of people included in the study. It was most frequently documented in people with bipolar disorder (22.6%), but was also common in personality disorder (17.8%) and schizophrenia (15.5%). It was associated with a greater number of days spent in hospital (B coefficient 18.5, 95% CI 12.1–24.8), greater frequency of hospitalisation (incidence rate ratio 1.95, 1.75–2.17), and an increased likelihood of prescription of antipsychotics (2.03, 1.75–2.35).
Conclusions: Using NLP, it was possible to identify mood instability in a large number of people, which would otherwise not have been possible by manually reading clinical records. Mood instability occurs in a wide range of mental disorders. It is generally associated with poor clinical outcomes. These findings suggest that clinicians should screen for mood instability across all common mental health disorders. The data also highlight the utility of NLP for clinical research.
Original language | English |
---|---|
Pages (from-to) | S265-S266 |
Journal | European Psychiatry |
Volume | 33 |
DOIs | |
Publication status | Published - 14 Mar 2016 |
Fingerprint
Dive into the research topics of 'Mood Instability and Clinical Outcomes in Mental Health Disorders: A Natural Language Processing (NLP) Study'. Together they form a unique fingerprint.Projects
- 1 Finished
-
Predicting clinical and functional outcomes in psychosis using machine learning.
Patel, R. (Primary Investigator), Dazzan, P. (Co-Investigator), McGuire, P. (Co-Investigator), Mechelli, A. (Co-Investigator) & Stewart, R. (Co-Investigator)
14/01/2013 → 13/01/2016
Project: Research