H Jin1, S Wu, I Vidyanti, P Di Capua, B Wu. 1. 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.
Abstract
INTRODUCTION: 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. OBJECTIVES: 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.
INTRODUCTION: 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 diabetespatients is a promising and valuable tool for providers to proactively assess depressive symptoms and identify those with depression. OBJECTIVES: 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: Shinyi Wu; Kathleen Ell; Haomiao Jin; Irene Vidyanti; Chih-Ping Chou; Pey-Jiuan Lee; Sandra Gross-Schulman; Laura Myerchin Sklaroff; David Belson; Arthur M Nezu; Joel Hay; Chien-Ju Wang; Geoffrey Scheib; Paul Di Capua; Caitlin Hawkins; Pai Liu; Magaly Ramirez; Brian W Wu; Mark Richman; Caitlin Myers; Davin Agustines; Robert Dasher; Alex Kopelowicz; Joseph Allevato; Mike Roybal; Eli Ipp; Uzma Haider; Sharon Graham; Vahid Mahabadi; Jeffrey Guterman Journal: J Med Internet Res Date: 2018-04-23 Impact factor: 5.428
Authors: Tania Guadalupe Gómez-Peralta; Thelma Beatriz González-Castro; Ana Fresan; Carlos Alfonso Tovilla-Zárate; Isela Esther Juárez-Rojop; Mario Villar-Soto; Yazmín Hernández-Díaz; María Lilia López-Narváez; Jorge L Ble-Castillo; Nonanzit Pérez-Hernández; José Manuel Rodríguez-Pérez Journal: Int J Environ Res Public Health Date: 2018-06-07 Impact factor: 3.390
Authors: Wendy Marie Ingram; Anna M Baker; Christopher R Bauer; Jason P Brown; Fernando S Goes; Sharon Larson; Peter P Zandi Journal: Neurol Psychiatry Brain Res Date: 2020-02-21