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

Abstract

BACKGROUND Background: Mental illness(MI) is common among those who work in healthcare settings. Whether MI is related to employees’ mental status at work has yet been known. 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 The aim of this study is to build a model using the convolutional neural networks(CNN) and fit statistics based on two aspects of measures and Outfit mean square errors(MNSQ) for 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 the ability to assess and understand their own mental status with an app on their mobile device. METHODS We recruited 352 respiratory therapists (RT) 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 four classes(i.e., MI, false MI, health, and false health) and (2) building an ELMH predictive model to estimate 108 parameters of the CMM model. We calculated the prediction accuracy rate and created an app for classifying MI for RTs at the workplace as an online assessment. RESULTS We observed that (1) eight domains in ELMH were retained by EFA; (2) four types of mental health (n = 6, 63, 265, and 18 located in four quadrants) were classified by using 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 the 108 parameters estimated by using CNN for improving the accuracy of mental health for RTs. An MI app developed for helping RTs self-detect work-related MI at an early stage should be made more available and viable in the future. CLINICALTRIAL Not available