title: Predicting Depression among Patients with Diabetes Using Longitudinal Data. A Multilevel Regression Model.
abstract: BACKGROUND
  This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare".
  
  
  BACKGROUND
  Depression is a common and often undiagnosed condition for patients with diabetes. It is also a condition that significantly impacts healthcare outcomes, use, and cost as well as elevating suicide risk. Therefore, a model to predict depression among diabetes patients is a promising and valuable tool for providers to proactively assess depressive symptoms and identify those with depression.
  
  
  OBJECTIVE
  This study seeks to develop a generalized multilevel regression model, using a longitudinal data set from a recent large-scale clinical trial, to predict depression severity and presence of major depression among patients with diabetes.
  
  
  METHODS
  Severity of depression was measured by the Patient Health Questionnaire PHQ-9 score. Predictors were selected from 29 candidate factors to develop a 2-level Poisson regression model that can make population-average predictions for all patients and subject-specific predictions for individual patients with historical records. Newly obtained patient records can be incorporated with historical records to update the prediction model. Root-mean-square errors (RMSE) were used to evaluate predictive accuracy of PHQ-9 scores. The study also evaluated the classification ability of using the predicted PHQ-9 scores to classify patients as having major depression.
  
  
  RESULTS
  Two time-invariant and 10 time-varying predictors were selected for the model. Incorporating historical records and using them to update the model may improve both predictive accuracy of PHQ-9 scores and classification ability of the predicted scores. Subject-specific predictions (for individual patients with historical records) achieved RMSE about 4 and areas under the receiver operating characteristic (ROC) curve about 0.9 and are better than population-average predictions.
  
  
  CONCLUSIONS
  The study developed a generalized multilevel regression model to predict depression and demonstrated that using generalized multilevel regression based on longitudinal patient records can achieve high predictive ability.
authors.affiliation: Shinyi Wu, PhD, School of Social Work and Epstein Department of Industrial and Systems Engineering, University of Southern California, Edward R. Roybal Institute on Aging, 1150 South Olive Street, Suite 1400, Los Angeles, CA 90015, USA, E-mail: shinyiwu@usc.edu.
authors.forename: H
authors.initials: H
authors.lastname: Jin
authors.forename: S
authors.initials: S
authors.lastname: Wu
authors.forename: I
authors.initials: I
authors.lastname: Vidyanti
authors.forename: P
authors.initials: P
authors.lastname: Di Capua
authors.forename: B
authors.initials: B
authors.lastname: Wu
mesh_terms: D002140:California / Q000453:epidemiology; D015984:Causality; D003198:Computer Simulation; D020000:Decision Support Systems, Clinical / Q000458:organization & administration; D003863:Depression / Q000175:diagnosis* / Q000453:epidemiology*; D048909:Diabetes Complications / Q000175:diagnosis* / Q000453:epidemiology*; D057286:Electronic Health Records / Q000145:classification* / Q000706:statistics & numerical data; D006801:Humans; D008137:Longitudinal Studies; D000069550:Machine Learning; D009323:Natural Language Processing; D015995:Prevalence; D011379:Prognosis; D016016:Proportional Hazards Models; D012044:Regression Analysis; D015203:Reproducibility of Results; D012307:Risk Factors; D012680:Sensitivity and Specificity
keywords: Depression; comorbidity; diabetes mellitus; machine learning; multilevel regression