AIMC Topic: Pressure Ulcer
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Machine learning evaluation of inequities and disparities associated with nurse sensitive indicator safety events.
Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
May 21, 2024
PURPOSE: To use machine learning to examine health equity and clinical outcomes in patients who experienced a nurse sensitive indicator (NSI) event, defined as a fall, a hospital-acquired pressure injury (HAPI) or a hospital-acquired infection (HAI).
Performance of artificial intelligence chatbots in interpreting clinical images of pressure injuries.
Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair Society
May 15, 2024
To evaluate the accuracy of AI chatbots in staging pressure injuries according to the National Pressure Injury Advisory Panel (NPIAP) Staging through clinical image interpretation, a cross-sectional design was conducted to assess five leading publicl...
Prediction model of pressure injury occurrence in diabetic patients during ICU hospitalization--XGBoost machine learning model can be interpreted based on SHAP.
Intensive & critical care nursing
May 2, 2024
BACKGROUND: The occurrence of pressure injury in patients with diabetes during ICU hospitalization can result in severe complications, including infections and non-healing wounds.
Applying Object Detection and Large Language Model to Establish a Smart Telemedicine Diagnosis System with Chatbot: A Case Study of Pressure Injuries Diagnosis System.
Telemedicine journal and e-health : the official journal of the American Telemedicine Association
Mar 21, 2024
The scarcity of medical resources and personnel has worsened due to COVID-19. Telemedicine faces challenges in assessing wounds without physical examination. Evaluating pressure injuries is time consuming, energy intensive, and inconsistent. Most of...
A machine learning algorithm-based predictive model for pressure injury risk in emergency patients: A prospective cohort study.
International emergency nursing
Mar 2, 2024
OBJECTIVES: To construct pressure injury risk prediction models for emergency patients based on different machine learning algorithms, to optimize the best model, and to provide a suitable assessment tool for preventing the occurrence of pressure inj...
Evaluating Natural Language Processing Packages for Predicting Hospital-Acquired Pressure Injuries From Clinical Notes.
Computers, informatics, nursing : CIN
Mar 1, 2024
Incidence of hospital-acquired pressure injury, a key indicator of nursing quality, is directly proportional to adverse outcomes, increased hospital stays, and economic burdens on patients, caregivers, and society. Thus, predicting hospital-acquired ...
Enhancing Pressure Injury Surveillance Using Natural Language Processing.
Journal of patient safety
Dec 26, 2023
OBJECTIVE: This study assessed the feasibility of nursing handoff notes to identify underreported hospital-acquired pressure injury (HAPI) events.
Augmented Decision-Making in wound Care: Evaluating the clinical utility of a Deep-Learning model for pressure injury staging.
International journal of medical informatics
Oct 17, 2023
BACKGROUND: Precise categorization of pressure injury (PI) stages is critical in determining the appropriate treatment for wound care. However, the expertise necessary for PI staging is frequently unavailable in residential care settings.
Risk predictions of hospital-acquired pressure injury in the intensive care unit based on a machine learning algorithm.
International wound journal
Jun 13, 2023
Pressure injury (PI), or local damage to soft tissues and skin caused by prolonged pressure, remains controversial in the medical world. Patients in intensive care units (ICUs) were frequently reported to suffer PIs, with a heavy burden on their life...
Leveraging artificial intelligence and decision support systems in hospital-acquired pressure injuries prediction: A comprehensive review.
Artificial intelligence in medicine
Apr 25, 2023
BACKGROUND: Hospital-acquired pressure injuries (HAPIs) constitute a significant challenge harming thousands of people worldwide yearly. While various tools and methods are used to identify pressure injuries, artificial intelligence (AI) and decision...
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