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Characterizing DSM-5 and ICD-11 personality disorder features in psychiatric inpatients at scale using electronic health records

Published online by Cambridge University Press:  23 September 2019

Sergio A. Barroilhet Open the ORCID record for Sergio A. Barroilhet [Opens in a new window] ,
Amelia M. Pellegrini Open the ORCID record for Amelia M. Pellegrini [Opens in a new window] ,
Thomas H. McCoy Open the ORCID record for Thomas H. McCoy [Opens in a new window]  and
Roy H. Perlis Open the ORCID record for Roy H. Perlis [Opens in a new window]
Sergio A. Barroilhet
Affiliation:
Center for Quantitative Health, Division of Clinical Research and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA Department of Psychiatry, Tufts University School of Medicine, Boston, MA, USA University Psychiatric Clinic, University of Chile Clinical Hospital, Santiago, Chile
Amelia M. Pellegrini
Affiliation:
Center for Quantitative Health, Division of Clinical Research and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
Thomas H. McCoy
Affiliation:
Center for Quantitative Health, Division of Clinical Research and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
Roy H. Perlis*
Affiliation:
Center for Quantitative Health, Division of Clinical Research and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
*
Author for correspondence: Roy H. Perlis, E-mail: rperlis@partners.org
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Abstract

Background

Investigation of personality traits and pathology in large, generalizable clinical cohorts has been hindered by inconsistent assessment and failure to consider a range of personality disorders (PDs) simultaneously.

Methods

We applied natural language processing (NLP) of electronic health record notes to characterize a psychiatric inpatient cohort. A set of terms reflecting personality trait domains were derived, expanded, and then refined based on expert consensus. Latent Dirichlet allocation was used to score notes to estimate the extent to which any given note reflected PD topics. Regression models were used to examine the relationship of these estimates with sociodemographic features and length of stay.

Results

Among 3623 patients with 4702 admissions, being male, non-white, having a low burden of medical comorbidity, being admitted through the emergency department, and having public insurance were independently associated with greater levels of disinhibition, detachment, and psychoticism. Being female, white, and having private insurance were independently associated with greater levels of negative affectivity. The presence of disinhibition, psychoticism, and negative affectivity were each significantly associated with a longer stay, while detachment was associated with a shorter stay.

Conclusions

Personality features can be systematically and scalably measured using NLP in the inpatient setting, and some of these features associate with length of stay. Developing treatment strategies for patients scoring high in certain personality dimensions may facilitate more efficient, targeted interventions, and may help reduce the impact of personality features on mental health service utilization.

Keywords

Electronic health recordlength of staymachine learningnatural language processingpersonality disorder
Type
Original Articles
Information
Psychological Medicine , Volume 50 , Issue 13 , October 2020 , pp. 2221 - 2229
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Copyright
Copyright © Cambridge University Press 2019

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