Abstract
Machine learning has been suggested as a means of identifying individuals at greatest risk for hospital readmission, including psychiatric readmission. We sought to compare the performance of predictive models that use interpretable representations derived via topic modeling to the performance of human experts and nonexperts. We examined all 5076 admissions to a general psychiatry inpatient unit between 2009 and 2016 using electronic health records. We developed multiple models to predict 180-day readmission for these admissions based on features derived from narrative discharge summaries, augmented by baseline sociodemographic and clinical features. We developed models using a training set comprising 70% of the cohort and evaluated on the remaining 30%. Baseline models using demographic features for prediction achieved an area under the curve (AUC) of 0.675 [95% CI 0.674–0.676] on an independent testing set, while language-based models also incorporating bag-of-words features, discharge summaries topics identified by Latent Dirichlet allocation (LDA), and prior psychiatric admissions achieved AUC of 0.726 [95% CI 0.725–0.727]. To characterize the difficulty of the task, we also compared the performance of these classifiers to both expert and nonexpert human raters, with and without feedback, on a subset of 75 test cases. These models outperformed humans on average, including predictions by experienced psychiatrists. Typical note tokens or topics associated with readmission risk were related to pregnancy/postpartum state, family relationships, and psychosis.
Highlights
1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; Introduction The ability of prediction tools based on machine learning (ML) to identify high-risk individuals among clinical populations has been embraced across health care[1]
Clinical populations are rarely so selected, which may pose challenges for natural language processing methods. How well could such models account for populations that are both medically and psychiatrically complex? Second, while prior work indicates improvement over chance, a more relevant question is performance compared to human experts: how well do clinicians perform on a given task, and can ML outperform human learning?
Machine learning The training set was randomly split into 10 folds to perform cross-validation, which was carried out separately with each of the models (SVM, LR, XGB, and MLP) for the feature representations listed in Methods section above
Summary
The ability of prediction tools based on machine learning (ML) to identify high-risk individuals among clinical populations has been embraced across health care[1]. A number of reports suggest the ability of these approaches to improve on chance in predicting hospital readmission, suicide attempts, or mortality, for example[2,3]. We demonstrated that incorporation of features based on topic modeling of several important questions remain unanswered by prior work. How well could such models account for populations that are both medically and psychiatrically complex? While prior work indicates improvement over chance, a more relevant question is performance compared to human experts: how well do clinicians perform on a given task, and can ML outperform human learning? How well could such models account for populations that are both medically and psychiatrically complex? Second, while prior work indicates improvement over chance, a more relevant question is performance compared to human experts: how well do clinicians perform on a given task, and can ML outperform human learning?
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