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Transl Psychiatry. 2016 Oct 18;6(10):e921. doi: 10.1038/tp.2015.182.

Predicting early psychiatric readmission with natural language processing of narrative discharge summaries.

Author information

MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA.
Department of Computer Science, University of Massachusetts Lowell, Lowell, MA, USA.
Center for Experimental Drugs and Diagnostics, Massachusetts General Hospital, Boston, MA, USA.
Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA.
Partners Research Information Systems and Computing, Partners HealthCare System, Boston, MA, USA.


The ability to predict psychiatric readmission would facilitate the development of interventions to reduce this risk, a major driver of psychiatric health-care costs. The symptoms or characteristics of illness course necessary to develop reliable predictors are not available in coded billing data, but may be present in narrative electronic health record (EHR) discharge summaries. We identified a cohort of individuals admitted to a psychiatric inpatient unit between 1994 and 2012 with a principal diagnosis of major depressive disorder, and extracted inpatient psychiatric discharge narrative notes. Using these data, we trained a 75-topic Latent Dirichlet Allocation (LDA) model, a form of natural language processing, which identifies groups of words associated with topics discussed in a document collection. The cohort was randomly split to derive a training (70%) and testing (30%) data set, and we trained separate support vector machine models for baseline clinical features alone, baseline features plus common individual words and the above plus topics identified from the 75-topic LDA model. Of 4687 patients with inpatient discharge summaries, 470 were readmitted within 30 days. The 75-topic LDA model included topics linked to psychiatric symptoms (suicide, severe depression, anxiety, trauma, eating/weight and panic) and major depressive disorder comorbidities (infection, postpartum, brain tumor, diarrhea and pulmonary disease). By including LDA topics, prediction of readmission, as measured by area under receiver-operating characteristic curves in the testing data set, was improved from baseline (area under the curve 0.618) to baseline+1000 words (0.682) to baseline+75 topics (0.784). Inclusion of topics derived from narrative notes allows more accurate discrimination of individuals at high risk for psychiatric readmission in this cohort. Topic modeling and related approaches offer the potential to improve prediction using EHRs, if generalizability can be established in other clinical cohorts.

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