Airborne Influenza Virus in Daycare Centers.

In this study, we investigated the concentration of airborne influenza virus in daycare centers and influencing factors, such as common cold prevalence, air pollutants, and meteorological factors. A total of 209 air samples were collected from daycare centers in Kaohsiung and the influenza virus was analyzed using real-time quantitative polymerase chain reaction. Air pollutants and metrological factors were measured using real-time monitoring equipment.

An App Developed for Detecting Nurse Burnouts Using the Convolutional Neural Networks in Microsoft Excel: Population-Based Questionnaire Study.

Background: Burnout (BO), a critical syndrome particularly for nurses in health care settings, substantially affects their physical and psychological status, the institute's well-being, and indirectly, patient outcomes. However, objectively classifying BO levels has not been defined and noticed in the literature.

Objective: The aim of this study is to build a model using the convolutional neural network (CNN) to develop an app for automatic detection and classification of nurse BO using the Maslach Burnout Inventory-Human Services Survey (MBI-HSS) to help assess nurse BO at an earlier stage.

Constructing Inpatient Pressure Injury Prediction Models Using Machine Learning Techniques.

The incidence rate of pressure injury is a critical nursing quality indicator in clinic care; consequently, factors causing pressure injury are diverse and complex. The early prevention of pressure injury and monitoring of these complex high-risk factors are critical to reduce the patients' pain, prevent further surgical treatment, avoid prolonged hospital stay, decrease the risk of wound infection, and lower associated medical costs and expenses. Although a number of risk assessment scales of pressure injury have been adopted in various countries, their criteria are set for specific populations, which may not be suitable for the medical care systems of other countries.

Detecting hospital behaviors of up-coding on DRGs using Rasch model of continuous variables and online cloud computing in Taiwan.

Background: This work aims to apply data-detection algorithms to predict the possible deductions of reimbursement from Taiwan's Bureau of National Health Insurance (BNHI), and to design an online dashboard to send alerts and reminders to physicians after completing their patient discharge summaries.

Methods: Reimbursement data for discharged patients were extracted from a Taiwan medical center in 2016. Using the Rasch model of continuous variables, we applied standardized residual analyses to 20 sets of norm-referenced diagnosis-related group (DRGs), each with 300 cases, and compared these to 194 cases with deducted records from the BNHI.

Application of a multidimensional computerized adaptive test for a Clinical Dementia Rating Scale through computer-aided techniques.

Background: With the increasingly rapid growth of the elderly population, individuals aged 65 years and above now compose 14% of Taiwanese citizens, thereby making Taiwanese society an aged society. A leading factor that affects the elderly population is dementia. A method of precisely and efficiently examining patients with dementia through multidimensional computer adaptive testing (MCAT) to accurately determine the patients' stage of dementia needs to be developed.