Predicting Depression among Patients with Diabetes Using Longitudinal Data
| Autor: | Shinyi Wu, Irene Vidyanti, Brian Wu, Haomiao Jin, P. Di Capua |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
medicine.medical_specialty
Health Informatics Sensitivity and Specificity California Diabetes Complications Machine Learning 03 medical and health sciences symbols.namesake 0302 clinical medicine Health Information Management Risk Factors Health care Prevalence medicine Electronic Health Records Humans Computer Simulation Longitudinal Studies 030212 general & internal medicine Poisson regression Psychiatry Depression (differential diagnoses) Natural Language Processing Proportional Hazards Models Advanced and Specialized Nursing 030505 public health Receiver operating characteristic Depression business.industry Proportional hazards model Reproducibility of Results Regression analysis Decision Support Systems Clinical Prognosis medicine.disease Comorbidity Causality Patient Health Questionnaire symbols Regression Analysis 0305 other medical science business Clinical psychology |
| Zdroj: | Methods of Information in Medicine. 54:553-559 |
| ISSN: | 2511-705X 0026-1270 |
| DOI: | 10.3414/me14-02-0009 |
| Popis: | SummaryIntroduction: 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. |
| Databáze: | OpenAIRE |
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