An App for Classifying Personal Mental Illness at Workplace Using Fit Statistics and Convolutional Neural Networks: Survey-Based Quantitative Study

doi:10.2196/17857

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Please use this identifier to cite or link to this item: https://ir.cnu.edu.tw/handle/310902800/34068

Title:	An App for Classifying Personal Mental Illness at Workplace Using Fit Statistics and Convolutional Neural Networks: Survey-Based Quantitative Study
Authors:	Yan, Yu-Hua Chien, Tsair-Wei Yeh, Yu-Tsen Chou, Willy Hsing, Shu-Chen
Contributors:	Show Chwan Med Care Corp, Superintendent Off, Tainan Municipal Hosp Chia Nan Univ Pharm & Sci, Dept Hosp & Hlth Care Management Chi Mei Med Ctr, Dept Med Res St Georges Univ London, Med Sch Chung Shan Med Univ, Dept Phys Med & Rehabil Chiali Chi Mei Hosp, Dept Phys Med & Rehabil Chi Mei Med Ctr, Resp Therapy Unit
Keywords:	respiratory therapist ELMI app Rasch analysis convolutional neural network mental health mobile phone
Date:	2020
Issue Date:	2022-11-18 11:22:37 (UTC+8)
Publisher:	Jmir Publications, Inc
Abstract:	Background: Mental illness (MI) is common among those who work in health care settings. Whether MI is related to employees' mental status at work is yet to be determined. An MI app is developed and proposed to help employees assess their mental status in the hope of detecting MI at an earlier stage. Objective: This study aims to build a model using convolutional neural networks (CNNs) and fit statistics based on 2 aspects of measures and outfit mean square errors for the automatic detection and classification of personal MI at the workplace using the emotional labor and mental health (ELMH) questionnaire, so as to equip the staff in assessing and understanding their own mental status with an app on their mobile device. Methods: We recruited 352 respiratory therapists (RTs) working in Taiwan medical centers and regional hospitals to fill out the 44-item ELMH questionnaire in March 2019. The exploratory factor analysis (EFA), Rasch analysis, and CNN were used as unsupervised and supervised learnings for (1) dividing RTs into 4 classes (ie, MI, false MI, health, and false health) and (2) building an ELMH predictive model to estimate 108 parameters of the CNN model. We calculated the prediction accuracy rate and created an app for classifying MI for RTs at the workplace as a web-based assessment. Results: We observed that (1) 8 domains in ELMH were retained by EFA, (2) 4 types of mental health (n=6, 63, 265, and 18 located in 4 quadrants) were classified using the Rasch analysis, (3) the 44-item model yields a higher accuracy rate (0.92), and (4) an MI app available for RTs predicting MI was successfully developed and demonstrated in this study. Conclusions: The 44-item model with 108 parameters was estimated by using CNN to improve the accuracy of mental health for RTs. An MI app developed to help RTs self-detect work-related MI at an early stage should be made more available and viable in the future.
Relation:	Jmir Mhealth and Uhealth, v.8, n.7, pp.16
Appears in Collections:	[Dept. of Hospital and Health (including master's program)] Periodical Articles

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