ANALYSIS OF DEPRESSION TRAJECTORY PATTERNS USING COLLABORATIVE LEARNING

Purpose: Depression is a common, complex, and dynamic mental disorder. Mitigating depression has become a national health priority as it affects 1 out of 10 American adults and is the most common mental illness seen in primary care. While the emerging use of electronic health record (EHR) in health care provides an unprecedented information infrastructure, the complex dynamics of individual’s depression trajectory and the widely reported heterogeneity of the depression population are two major challenges for monitoring depressive patients. The objective of this study is to effectively analyze patterns in the collected depression trajectories of a treatment population and proactively probe new trajectories for monitoring treatment outcomes.

Method: Our data contain longitudinal Patient Health Questionnaire (PHQ)-9 scores over 4 years for assessing depression severity from the Mental Health Research Network. The PHQ-9 scores are linked to time between observations, type of providers, age, sex, treatment status, and Charlson comorbidity score of the patients. We analyzed >6,000 patients with at least four PHQ-9 observations who have on-going treatment. We first used smoothing splines to model each depression trajectory. We then used K-means clustering, recursive partitioning, and collaborative degradation model (CDM) to identify the subgroup patterns. CDM considers the underlying cluster structure embedded in the population and the resemblance of the individuals to these clusters. Lastly, for >3,000 patients with at least six PHQ-9 observations, we compared the individual growth model (IGM), mixed effect model (MEM), CDM, and CDM with network regularization (NCDM) on their predictive performance on the last two observations within each subject.

Result: We found five trajectory patterns in the on-going treatment population: stable high, stable low, stable moderate, an increasing and a decreasing group. The increasing and decreasing groups converge and become stable around a PHQ-9 score of 10 to 15. For prediction, the root mean square error in the testing set for IGM, MEM, CDM, and NCDM are 21.98, 6.12, 5.24, and 3.46.

Conclusion: We established a trajectory-based framework for depression diagnosis and prognosis adaptable to population heterogeneity using electronic health record data. Clustering provides an effective tool for characterizing the trajectory patterns of the depression population. For prediction, we found the NCDM achieved the highest performance.