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

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.

Using the IPcase Index with Inflection Points and the Corresponding Case Numbers to Identify the Impact Hit by COVID-19 in China: An Observation Study

“…The Epi in Equation (1) is determined by the known parameters a (=1) and b (=0) after performing Step E. The model residual is equal to 0 at the θ (=0) on the x -axis in this example. Perform the Solver add-in: The Microsoft Solver add-in [ 34 , 35 , 36 ] was used to estimate the model parameters ( Appendix B ). The OC can be plotted to predict the potential CNIC and to determine the IP days (see next section).…”

“…The Microsoft Solver add-in [ 34 , 35 , 36 ] was used to estimate the model parameters ( Appendix B ). The OC can be plotted to predict the potential CNIC and to determine the IP days (see next section).…”

“…Third, using the Microsoft Solver add-in is a common approach, but only a few were shown in previous studies [34][35][36] to model the epidemic situation for each region. Data and model building videos in Microsoft Excel are provided in Appendices A and C. The approach of searching IP days for regions affected by COVID-19 is easy to understand.…”

An online time-to-event dashboard comparing the effective control of COVID-19 among continents using the inflection point on an ogive curve

“…The Microsoft Solver add-in [ 41 – 43 ] was performed to estimate model parameters; see Multimedia Appendix 2. The ogive curve can be used for determining the IP and predicting the future NCIC (see the next section).…”

An App Developed for Predicting Nurse Intention to Quit the Job (NIQJ) Using the Artificial Neural Networks(ANN) in Microsoft Excel: Population-Based Questionnaire Study

“…The ANN and convolutional neural network (CNN) were analyzed with the four scenarios mentioned above. CNN has traditionally been performed on Microsoft (MS) Excel [18,20,22] while ANN has not been paired along with MS Excel in the past. As demonstrated in gure 2 below, the ANN process involves data input in layer 1 where the data joined with two types of parameters and run through the sigmoid function algorithms in layers 2 and 3.…”