Individualized prediction of depressive disorder in the elderly : A multitask deep learning approach

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

29 Scopus Citations
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  • Zhongzhi Xu
  • Qingpeng Zhang
  • Wentian Li
  • Mingyang Li
  • Paul Siu Fai Yip

Related Research Unit(s)


Original languageEnglish
Article number103973
Journal / PublicationInternational Journal of Medical Informatics
Online published23 Sept 2019
Publication statusPublished - Dec 2019


Introduction: Depressive disorder is one of the major public health problems among the elderly. An effective depression risk prediction model can provide insights on the disease progression and potentially inform timely targeted interventions. Therefore, research on predicting the onset of depressive disorder for elderly adults considering the sequential progression patterns is critically needed. 
Objective: This research aims to develop a state-of-the-art deep learning model for the individualized prediction of depressive disorder with a 22-year longitudinal survey data among elderly people in the United States. 
Methods: We obtain the 22-year longitudinal survey data from the University of Michigan Health and Retirement Study, which consists of information on 20,000 elderly people in the United States from 1992 to 2014. To capture temporal and high-order interactions among risk factors, the proposed deep learning model utilizes a recurrent neural network framework with a multitask structure. The C-statistic and the mean absolute error are used to evaluate the prediction accuracy of the proposed model and a set of baseline models. 
Results: The experiments with the 22-year longitudinal survey data indicate that (a) machine learning models can provide an accurate prediction of the onset of depressive disorder for elderly individuals; (b) the temporal patterns of risk factors are associated with the onset of depressive disorder; and (c) the proposed multitask deep learning model exhibits superior performance as compared with baseline models. 
Conclusion: The results demonstrate the capability of deep learning-based prediction models in capturing temporal and high-order interactions among risk factors, which are usually ignored by traditional regression models. This research sheds light on the use of machine learning models to predict the onset of depressive disorder among elderly people. Practically, the proposed methods can be implemented as a decision support system to help clinicians make decisions and inform actionable intervention strategies for elderly people.

Research Area(s)

  • Deep learning, Depression, Depressive disorder prediction, Patient progression model