Investigating Mood Instability in Psychotic Disorders Using Natural Language Processing (NLP)

Rashmi Patel, Theodore Lloyd, Richard George Jackson, Michael Ball, Hitesh Shetty, Matthew Broadbent, John Geddes, Robert James Stewart, Philip McGuire, Matthew John Taylor

Research output: Contribution to journalMeeting abstractpeer-review


Purpose: Mood instability is an important clinical problem but it 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). We extracted mood instability data from EHRs using NLP and investigated its impact on clinical outcomes.

Materials and Methods: Data on mood instability were obtained from 27,704 adults receiving care from the South London and Maudsley NHS Foundation Trust (SLaM) for affective and psychotic disorders. These data were used to investigate the association of mood instability with hospitalisation and treatment outcomes.

Results: Mood instability was present 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).

Conclusion: 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 including psychotic disorders. It is generally associated with poor clinical outcomes. These findings suggest that clinicians should screen for mood instability across all mental health disorders, including people with psychotic disorders.
Original languageEnglish
Article numbereip.12396
Pages (from-to)106
Number of pages1
JournalEarly Intervention in Psychiatry
Issue numberS1
Publication statusPublished - 3 Oct 2016


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