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Text-mining occupations from the mental health electronic health record: a natural language processing approach using records from the Clinical Record Interactive Search (CRIS) platform in south London, UK.

Research output: Contribution to journalArticle

Original languageEnglish
JournalBMJ Open
Accepted/In press10 Nov 2020

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Abstract

Objectives We set out to develop, evaluate, and implement a novel application using natural language processing to text-mine occupations from the free-text of psychiatric clinical notes. Design Development and validation of a natural language processing application using General Architecture for Text Engineering (GATE) software to extract occupations from de-identified clinical records. Setting & Participants Electronic health records from a large secondary mental health provider in south London, accessed through the Clinical Record Interactive Search (CRIS) platform. The text-mining application was run over the free-text fields in the electronic health records of 341,720 patients (all aged ≥16). Outcomes Precision and recall estimates of the application performance; occupation retrieval using the application compared to structured fields; most common patient occupations; and analysis of key sociodemographic and clinical indicators for occupation recording. Results Using the structured fields alone, only 14% of patients had occupation recorded. By implementing the text-mining application in addition to the structured fields, occupations were identified in 57% of patients. The application performed on gold-standard human-annotated clinical text at a precision level of 0.79 and recall level of 0.77. The most common patient occupations recorded were ‘student’, and ‘unemployed’. Patients with more service contact were more likely to have an occupation recorded, as were patients of a male gender, older age, and those living in areas of lower deprivation. Conclusion This is the first time a natural language processing application has been used to successfully derive patient-level occupations from the free-text of electronic mental health records, performing with good levels of precision and recall, and applied at scale. This may be used to inform clinical studies relating to the broader social determinants of health using electronic health records.

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