Skip to main content Accessibility help
×
Home
Hostname: page-component-78bd46657c-2z7pd Total loading time: 0.276 Render date: 2021-05-08T10:57:46.267Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": false, "newCiteModal": false, "newCitedByModal": true }

Article contents

Mood instability and clinical outcomes in mental health disorders: A natural language processing (NLP) study

Published online by Cambridge University Press:  23 March 2020

R. Patel
Affiliation:
Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, London, United Kingdom
T. Lloyd
Affiliation:
Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, London, United Kingdom
R. Jackson
Affiliation:
Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, London, United Kingdom
M. Ball
Affiliation:
Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, London, United Kingdom
H. Shetty
Affiliation:
South London and Maudsley NHS Foundation Trust, Biomedical Research Centre Nucleus, London, United Kingdom
M. Broadbent
Affiliation:
South London and Maudsley NHS Foundation Trust, Biomedical Research Centre Nucleus, London, United Kingdom
J.R. Geddes
Affiliation:
University of Oxford, Department of Psychiatry, Oxford, United Kingdom
R. Stewart
Affiliation:
Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, London, United Kingdom
P. McGuire
Affiliation:
Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, London, United Kingdom
M. Taylor
Affiliation:
Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, London, United Kingdom
Get access

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.

Disclosure of interest

The authors have not supplied their declaration of competing interest.

Type
EW433
Copyright
Copyright © European Psychiatric Association 2014

Access options

Get access to the full version of this content by using one of the access options below.
Submit a response

Comments

No Comments have been published for this article.

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Mood instability and clinical outcomes in mental health disorders: A natural language processing (NLP) study
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

Mood instability and clinical outcomes in mental health disorders: A natural language processing (NLP) study
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

Mood instability and clinical outcomes in mental health disorders: A natural language processing (NLP) study
Available formats
×
×

Reply to: Submit a response


Your details


Conflicting interests

Do you have any conflicting interests? *