Predicting Depression among Patients with Diabetes Using Longitudinal Data. A Multilevel Regression Model

• Published in: Methods of information in medicine Vol. 54; no. 6; p. 553
• Main Authors: Jin, H, Wu, S, Vidyanti, I, Di Capua, P, Wu, B
• Format: Journal Article
• Language: English
• Published: Germany 2015
• Subjects: California - epidemiology; Causality; Computer Simulation; Decision Support Systems, Clinical - organization & administration; Depression - diagnosis; Depression - epidemiology; Diabetes Complications - diagnosis; Diabetes Complications - epidemiology; Electronic Health Records - classification; Electronic Health Records - statistics & numerical data; Humans; Longitudinal Studies; Machine Learning; Natural Language Processing; Prevalence; Prognosis; Proportional Hazards Models; Regression Analysis; Reproducibility of Results; Risk Factors; Sensitivity and Specificity; Depression; diabetes mellitus; comorbidity; multilevel regression; machine learning

This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". 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 a...