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

Research output: Contribution to journalArticle

David Chandran, Deborah Yumi Ahn, Chin-Kuo Chang, Hitesh Shetty, Jyoti Sanyal, Jonathan Muir Downs, Marcella Fok, Michael Ball, Richard George Jackson, Robert James Stewart, Hannah Naomi Rose Cohen, Jentien Vermeulen, Frederike Schirmbeck, Lieuwe De Haan, Richard Derek Hayes

Original languageEnglish
JournalScientific Reports
Publication statusAccepted/In press - 15 Aug 2019

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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.

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