Touchless ultraviolet disinfection (UVD) devices effectively reduce the bioburden of epidemiologically relevant pathogens, including Clostridium difficile. During a 25-month implementation period, UVD devices were deployed facilitywide for the termin...
OBJECTIVES: We validate a machine learning-based sepsis-prediction algorithm () for the detection and prediction of three sepsis-related gold standards, using only six vital signs. We evaluate robustness to missing data, customisation to site-specifi...
Length of stay (LOS) and discharge destination predictions are key parts of the discharge planning process for general medical hospital inpatients. It is possible that machine learning, using natural language processing, may be able to assist with ac...
Infection control and hospital epidemiology
33685546
OBJECTIVE: To investigate the feasibility of using an ultraviolet light-emitting diode (UV LED) robot for the terminal decontamination of coronavirus disease 2019 (COVID-19) patient rooms.
BACKGROUND: Retrospective studies have demonstrated that the deep learning-based cardiac arrest risk management system (DeepCARS™) is superior to the conventional methods in predicting in-hospital cardiac arrest (IHCA). This prospective study aimed t...
OBJECTIVES: The limitations of current early warning scores have prompted the development of deep learning-based systems, such as deep learning-based cardiac arrest risk management systems (DeepCARS). Unfortunately, in South Korea, only two instituti...
Early detection of deteriorating patients is important to prevent life-threatening events and improve clinical outcomes. Efforts have been made to detect or prevent major events such as cardiopulmonary resuscitation, but previously developed tools ar...