Pressure Injuries (PI) are one of the most common health conditions in the United States.
Most acute or long-term care patients are at risk of developing PI. Machine Learning (ML) …

Background: Hospital-acquired pressure injuries (HAPIs) constitute a significant challenge
harming thousands of people worldwide yearly. While various tools and methods are used …

The study aimed to establish a machine learning–based scoring nomogram for early
recognition of likely pressure injuries in an intensive care unit (ICU) using large‐scale …

Pressure injuries are increasing worldwide, and there has been no significant improvement
in preventing them. This study is aimed at reviewing and evaluating the studies related to the …

Hospital-Acquired Pressure Injury (HAPI), known as bedsore or decubitus ulcer, is one of the
most common health conditions in the United States. Machine learning has been used to …

Aims and objectives To summarize the use of machine learning (ML) for hospital‐acquired
pressure injury (HAPI) prediction and to systematically assess the performance and …

Aim The aim of this study was to analyse prevalence of pressure injury in intensive care
versus non‐intensive care patients. Background Hospital‐acquired pressure injury is an …

The relationship between body mass index and pressure ulcers in critically ill patients is
controversial. We aimed to investigate the association between body mass index and …

Background and Objectives: Bedsores/Pressure Injuries (PIs) are the second most common
diagnosis in healthcare system billing records in the United States and account for 60,000 …

Human action recognition (HAR), deeply rooted in computer vision, video surveillance,
automated observation, and human-computer interaction (HCI), enables precise …