title: Natural language processing to extract symptoms of severe mental illness from clinical text: the Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project.
abstract: We sought to use natural language processing to develop a suite of language models to capture key symptoms of severe mental illness (SMI) from clinical text, to facilitate the secondary use of mental healthcare data in research.
  
  
  
  Development and validation of information extraction applications for ascertaining symptoms of SMI in routine mental health records using the Clinical Record Interactive Search (CRIS) data resource; description of their distribution in a corpus of discharge summaries.
  
  
  
  Electronic records from a large mental healthcare provider serving a geographic catchment of 1.2 million residents in four boroughs of south London, UK.
  
  
  
  The distribution of derived symptoms was described in 23 128 discharge summaries from 7962 patients who had received an SMI diagnosis, and 13 496 discharge summaries from 7575 patients who had received a non-SMI diagnosis.
  
  
  
  Fifty SMI symptoms were identified by a team of psychiatrists for extraction based on salience and linguistic consistency in records, broadly categorised under positive, negative, disorganisation, manic and catatonic subgroups. Text models for each symptom were generated using the TextHunter tool and the CRIS database.
  
  
  
  We extracted data for 46 symptoms with a median F1 score of 0.88. Four symptom models performed poorly and were excluded. From the corpus of discharge summaries, it was possible to extract symptomatology in 87% of patients with SMI and 60% of patients with non-SMI diagnosis.
  
  
  
  This work demonstrates the possibility of automatically extracting a broad range of SMI symptoms from English text discharge summaries for patients with an SMI diagnosis. Descriptive data also indicated that most symptoms cut across diagnoses, rather than being restricted to particular groups.
authors.affiliation: Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
authors.forename: Richard G
authors.identifier: 0000-0002-3278-8547
authors.initials: RG
authors.lastname: Jackson
authors.affiliation: Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
authors.forename: Rashmi
authors.identifier: 0000-0002-9259-8788
authors.initials: R
authors.lastname: Patel
authors.affiliation: Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
authors.forename: Nishamali
authors.initials: N
authors.lastname: Jayatilleke
authors.affiliation: Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
authors.forename: Anna
authors.initials: A
authors.lastname: Kolliakou
authors.affiliation: Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
authors.forename: Michael
authors.initials: M
authors.lastname: Ball
authors.affiliation: Department of Computer Science, University of Sheffield, Sheffield, UK.
authors.forename: Genevieve
authors.initials: G
authors.lastname: Gorrell
authors.affiliation: Department of Computer Science, University of Sheffield, Sheffield, UK.
authors.forename: Angus
authors.initials: A
authors.lastname: Roberts
authors.affiliation: Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
authors.forename: Richard J
authors.identifier: 0000-0003-4224-9245
authors.initials: RJ
authors.lastname: Dobson
authors.affiliation: Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
authors.forename: Robert
authors.initials: R
authors.lastname: Stewart
mesh_terms: D002908:Chronic Disease; D003625:Data Collection; D016208:Databases, Factual; D057286:Electronic Health Records; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008131:London; D008490:Medical Informatics; D008491:Medical Informatics Applications; D001523:Mental Disorders / Q000175:diagnosis*; D009323:Natural Language Processing; D012042:Registries
keywords: MENTAL HEALTH; Natural Language Processing; Serious Mental Illness; Symptomatology; clinical informatics