عنوان انگلیسی مقاله:
Individualized prediction of depressive disorder in the elderly: A multitask deep learning approach
ترجمه فارسی عنوان مقاله:
پیش بینی فردی اختلال افسردگی در سالمندان: یک رویکرد یادگیری عمیق چند وظیفه ای
Sciencedirect - Elsevier - International Journal of Medical Informatics, 132 (2019) 103973: doi:10:1016/j:ijmedinf:2019:103973
Zhongzhi Xua, Qingpeng Zhanga,⁎, Wentian Lib, Mingyang Lic, Paul Siu Fai Yipd
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.
Keywords: Depressive disorder prediction | Depression | Deep learning | Patient progression model