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Natural language processing to extract symptoms of severe mental illness from clinical text: The Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project

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Richard G. Jackson ; Rashmi Patel ; Nishamali Jayatilleke ; Anna Kolliakou ; Michael Ball ; Genevieve Gorrell ; Angus Roberts ; Richard J. Dobson ; Robert Stewart

Original languageEnglish
Article numbere012012
Number of pages10
JournalBMJ Open
Volume7
Issue number1
DOIs
StatePublished - 1 Jan 2017

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Abstract

Objectives 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. Design 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. Setting Electronic records from a large mental healthcare provider serving a geographic catchment of 1.2 million residents in four boroughs of south London, UK. Participants The distribution of derived symptoms was described in 23128 discharge summaries from 7962 patients who had received an SMI diagnosis, and 13496 discharge summaries from 7575 patients who had received a non-SMI diagnosis. Outcome measures 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. Results 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. Conclusions 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.

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