Citation

Baek JW, Chung K. IEEE Access 2020; 8: 18171-18181.

Copyright

(Copyright © 2020, Institute of Electrical and Electronics Engineers)

DOI

10.1109/ACCESS.2020.2968393

PMID

unavailable

Abstract

Depression is a mental illness influenced by various factors, including stress in everyday life, physical activities, and physical diseases. It accompanies such symptoms as continuous depression, sleep disorder, and suicide attempts. In the healthcare, it is necessary to predict diverse situations accurately. Accordingly, in order to care for mental health, it is necessary to recognize individuals' situations and continue to manage them. In the area of mental diseases and treatment, research has been conducted to find a patient's state with the use of big data and to monitor the worst situation. Mental illnesses typically have depression. Research on Mental healthcare using artificial intelligence do conduct on prediction based on patients' voice, word choice, and conversation length. However, there is not much research on situation prediction in order to prevent depression. Therefore, this study proposes the context-DNN model for predicting depression risk using multiple-regression. The context of the proposed context-DNN consists of the information to predict situations and environments influencing depression in consideration of context information. Each context information related to predictor variables of depression becomes an input of DNN, and variable for depression prediction becomes an output of DNN. For DNN connection, the regression analysis to predict the risk of depression is used so as to predict the potential context influencing the risk of depression. According to the performance evaluation, the proposed model was evaluated to have the best performance in regression analysis and comparative analysis with DNN. © 2013 IEEE.

Language: en

Keywords

Regression analysis; mental health; Risk assessment; Mental health; Forecasting; Diseases; multiple regression; Patient treatment; Comparative analysis; Health care; Health risks; Deep learning; deep learning; context; context information; Context information; Deep neural network; Deep neural networks; depression risk; healthcare; Multiple regressions; Neural network model; Predictor variables; Situation prediction