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 …

Background Hospital-acquired pressure injuries remain a significant patient safety threat.
Current well-known pressure injury risk assessment tools have many limitations and …

Background Artificial intelligence (AI) is a broad outlet of computer science aimed at
constructing machines capable of simulating and performing tasks usually done by human …

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 …

Despite the fact that machine learning (ML) algorithms to construct predictive models for
pressure injury development are widely reported, the performance of the model remains …

For many acute and chronic pathologies, poor individual outcomes and overall statistics
reflect the current unsatisfactory state of healthcare. 1 Severe chronic pathologies such as …

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 …

Abstract Aims and Objectives To investigate the epidemiological characteristics of pressure
injury (PI) in older inpatients and predictive validity of the Braden scale. Design Cross …