Unsupervised Machine Learning to Identify Depressive Subtypes.

OBJECTIVES: This study evaluated an unsupervised machine learning method, latent Dirichlet allocation (LDA), as a method for identifying subtypes of depression within symptom data. METHODS: Data from 18,314 depressed patients were used to create LDA models. The outcomes included future emergency presentations, crisis events, and behavioral problems. One model was chosen for further analysis based upon its potential as a clinically meaningful construct. The associations between patient groups created with the final LDA model and outcomes were tested. These steps were repeated with a commonly-used latent variable model to provide additional context to the LDA results. RESULTS: Five subtypes were identified using the final LDA model. Prior to the outcome analysis, the subtypes were labeled based upon the symptom distributions they produced: psychotic, severe, mild, agitated, and anergic-apathetic. The patient groups largely aligned with the outcome data. For example, the psychotic and severe subgroups were more likely to have emergency presentations (odds ratio [OR] = 1.29; 95% confidence interval [CI], 1.17-1.43 and OR = 1.16; 95% CI, 1.05-1.29, respectively), whereas these outcomes were less likely in the mild subgroup (OR = 0.86; 95% CI, 0.78-0.94). We found that the LDA subtypes were characterized by clusters of unique symptoms. This contrasted with the latent variable model subtypes, which were largely stratified by severity. CONCLUSIONS: This study suggests that LDA can surface clinically meaningful, qualitative subtypes. Future work could be incorporated into studies concerning the biological bases of depression, thereby contributing to the development of new psychiatric therapeutics.

Collaborative mental health care: A narrative review.

The Collaborative Care model is a systematic strategy for treating behavioral health conditions in primary care through the integration of care managers and psychiatric consultants. Several randomized controlled trials have demonstrated that Collaborative Care increases access to mental health care and is more effective and cost efficient than the current standard of care for treating common mental illnesses. Large healthcare systems and organizations have begun to adopt Collaborative Care initiatives and are seeing improved treatment outcomes and provider and patient satisfaction. This review discusses current research on the effectiveness and cost-efficiency of Collaborative Care. In addition, this paper discusses its ability to adapt to specific patient populations, such as geriatrics, students, substance use, and women with perinatal depression, as well as the significance of measurement-based care and mental health screening in achieving improved clinical outcomes. Current data suggests that Collaborative Care may significantly improve patient outcomes and time-to-treatment in all reviewed settings, and successfully adapts to special patient populations. Despite the high upfront implementation burden of launching a Collaborative Care model program, these costs are generally offset by long term healthcare savings.

Identifying subtypes of depression in clinician-annotated text: a retrospective cohort study.

Current criteria for depression are imprecise and do not accurately characterize its distinct clinical presentations. As a result, its diagnosis lacks clinical utility in both treatment and research settings. Data-driven efforts to refine criteria have typically focused on a limited set of symptoms that do not reflect the disorder's heterogeneity. By contrast, clinicians often write about patients in depth, creating descriptions that may better characterize depression. However, clinical text is not commonly used to this end. Here we show that clinically relevant depressive subtypes can be derived from unstructured electronic health records. Five subtypes were identified amongst 18,314 patients with depression treated at a large mental healthcare provider by using unsupervised machine learning: severe-typical, psychotic, mild-typical, agitated, and anergic-apathetic. Subtypes were used to place patients in groups for validation; groups were found to be associated with future outcomes and characteristics that were consistent with the subtypes. These associations suggest that these categorizations are actionable due to their validity with respect to disease prognosis. Moreover, they were derived with automated techniques that might theoretically be widely implemented, allowing for future analyses in more varied populations and settings. Additional research, especially with respect to treatment response, may prove useful in further evaluation.