Objectives To identify negative symptoms in the clinical records of a large sample of patients with schizophrenia using natural language processing and assess their relationship with clinical outcomes.
Design Observational study using an anonymised electronic health record case register.
Setting South London and Maudsley NHS Trust (SLaM), a large provider of inpatient and community mental healthcare in the UK.
Participants 7678 patients with schizophrenia receiving care during 2011.
Main outcome measures Hospital admission, readmission and duration of admission.
Results 10 different negative symptoms were ascertained with precision statistics above 0.80. 41% of patients had 2 or more negative symptoms. Negative symptoms were associated with younger age, male gender and single marital status, and with increased likelihood of hospital admission (OR 1.24, 95% CI 1.10 to 1.39), longer duration of admission (β-coefficient 20.5 days, 7.6–33.5), and increased likelihood of readmission following discharge (OR 1.58, 1.28 to 1.95).
Conclusions Negative symptoms were common and associated with adverse clinical outcomes, consistent with evidence that these symptoms account for much of the disability associated with schizophrenia. Natural language processing provides a means of conducting research in large representative samples of patients, using data recorded during routine clinical practice.
- negative symptoms
- natural language processing
- electronic health records
- text mining
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