Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder

Abstract Obsessive and Compulsive Symptoms (OCS) or Obsessive Compulsive Disorder (OCD) in the context of schizophrenia or related disorders are of clinical importance as these are associated with a range of adverse outcomes. Natural Language Processing (NLP) applied to Electronic Health Records (EH...

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Autores principales:	David Chandran, Deborah Ahn Robbins, Chin-Kuo Chang, Hitesh Shetty, Jyoti Sanyal, Johnny Downs, Marcella Fok, Michael Ball, Richard Jackson, Robert Stewart, Hannah Cohen, Jentien M. Vermeulen, Frederike Schirmbeck, Lieuwe de Haan, Richard Hayes
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/bddb23e369c644198c71bb9c40dd80e0
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spelling	oai:doaj.org-article:bddb23e369c644198c71bb9c40dd80e02021-12-02T15:09:39ZUse of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder10.1038/s41598-019-49165-22045-2322https://doaj.org/article/bddb23e369c644198c71bb9c40dd80e02019-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-49165-2https://doaj.org/toc/2045-2322Abstract Obsessive and Compulsive Symptoms (OCS) or Obsessive Compulsive Disorder (OCD) in the context of schizophrenia or related disorders are of clinical importance as these are associated with a range of adverse outcomes. Natural Language Processing (NLP) applied to Electronic Health Records (EHRs) presents an opportunity to create large datasets to facilitate research in this area. This is a challenging endeavour however, because of the wide range of ways in which these symptoms are recorded, and the overlap of terms used to describe OCS with those used to describe other conditions. We developed an NLP algorithm to extract OCS information from a large mental healthcare EHR data resource at the South London and Maudsley NHS Foundation Trust using its Clinical Record Interactive Search (CRIS) facility. We extracted documents from individuals who had received a diagnosis of schizophrenia, schizoaffective disorder, or bipolar disorder. These text documents, annotated by human coders, were used for developing and refining the NLP algorithm (600 documents) with an additional set reserved for final validation (300 documents). The developed NLP algorithm utilized a rules-based approach to identify each of symptoms associated with OCS, and then combined them to determine the overall number of instances of OCS. After its implementation, the algorithm was shown to identify OCS with a precision and recall (with 95% confidence intervals) of 0.77 (0.65–0.86) and 0.67 (0.55–0.77) respectively. The development of this application demonstrated the potential to extract complex symptomatic data from mental healthcare EHRs using NLP to facilitate further analyses of these clinical symptoms and their relevance for prognosis and intervention response.David ChandranDeborah Ahn RobbinsChin-Kuo ChangHitesh ShettyJyoti SanyalJohnny DownsMarcella FokMichael BallRichard JacksonRobert StewartHannah CohenJentien M. VermeulenFrederike SchirmbeckLieuwe de HaanRichard HayesNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-7 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q David Chandran Deborah Ahn Robbins Chin-Kuo Chang Hitesh Shetty Jyoti Sanyal Johnny Downs Marcella Fok Michael Ball Richard Jackson Robert Stewart Hannah Cohen Jentien M. Vermeulen Frederike Schirmbeck Lieuwe de Haan Richard Hayes Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder
description	Abstract Obsessive and Compulsive Symptoms (OCS) or Obsessive Compulsive Disorder (OCD) in the context of schizophrenia or related disorders are of clinical importance as these are associated with a range of adverse outcomes. Natural Language Processing (NLP) applied to Electronic Health Records (EHRs) presents an opportunity to create large datasets to facilitate research in this area. This is a challenging endeavour however, because of the wide range of ways in which these symptoms are recorded, and the overlap of terms used to describe OCS with those used to describe other conditions. We developed an NLP algorithm to extract OCS information from a large mental healthcare EHR data resource at the South London and Maudsley NHS Foundation Trust using its Clinical Record Interactive Search (CRIS) facility. We extracted documents from individuals who had received a diagnosis of schizophrenia, schizoaffective disorder, or bipolar disorder. These text documents, annotated by human coders, were used for developing and refining the NLP algorithm (600 documents) with an additional set reserved for final validation (300 documents). The developed NLP algorithm utilized a rules-based approach to identify each of symptoms associated with OCS, and then combined them to determine the overall number of instances of OCS. After its implementation, the algorithm was shown to identify OCS with a precision and recall (with 95% confidence intervals) of 0.77 (0.65–0.86) and 0.67 (0.55–0.77) respectively. The development of this application demonstrated the potential to extract complex symptomatic data from mental healthcare EHRs using NLP to facilitate further analyses of these clinical symptoms and their relevance for prognosis and intervention response.
format	article
author	David Chandran Deborah Ahn Robbins Chin-Kuo Chang Hitesh Shetty Jyoti Sanyal Johnny Downs Marcella Fok Michael Ball Richard Jackson Robert Stewart Hannah Cohen Jentien M. Vermeulen Frederike Schirmbeck Lieuwe de Haan Richard Hayes
author_facet	David Chandran Deborah Ahn Robbins Chin-Kuo Chang Hitesh Shetty Jyoti Sanyal Johnny Downs Marcella Fok Michael Ball Richard Jackson Robert Stewart Hannah Cohen Jentien M. Vermeulen Frederike Schirmbeck Lieuwe de Haan Richard Hayes
author_sort	David Chandran
title	Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder
title_short	Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder
title_full	Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder
title_fullStr	Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder
title_full_unstemmed	Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder
title_sort	use of natural language processing to identify obsessive compulsive symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/bddb23e369c644198c71bb9c40dd80e0
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Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder

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