Characterizing DSM-5 and ICD-11 personality disorder features in psychiatric inpatients at scale using electronic health records
Author
dc.contributor.author
Barroilhet, Sergio A.
Author
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Pellegrini, Amelia M.
Author
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McCoy, Thomas H.
Author
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Perlis, Roy H.
Admission date
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2021-01-13T21:48:15Z
Available date
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2021-01-13T21:48:15Z
Publication date
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2020
Cita de ítem
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Psychological Medicine Volumen: 50 Número: 13 Páginas: 2221-2229 Oct 2020
es_ES
Identifier
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10.1017/S0033291719002320
Identifier
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https://repositorio.uchile.cl/handle/2250/178238
Abstract
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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.
es_ES
Patrocinador
dc.description.sponsorship
United States Department of Health & Human Services
National Institutes of Health (NIH) - USA
NIH National Institute of Mental Health (NIMH)
1R01MH106577