AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Pressure Ulcer

Showing 31 to 40 of 55 articles

Clear Filters

Automatic measurement of pressure ulcers using Support Vector Machines and GrabCut.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Pressure ulcers are regions of trauma caused by a continuous pressure applied to soft tissues between a bony prominence and a hard surface. The manual monitoring of their healing evolution can be achieved by area assessment ...

A model for predicting 7-day pressure injury outcomes in paediatric patients: A machine learning approach.

Journal of advanced nursing
AIMS: We sought to explore factors associated with early pressure injury progression and build a model for predicting these outcomes using a machine learning approach.

Predicting the Development of Surgery-Related Pressure Injury Using a Machine Learning Algorithm Model.

The journal of nursing research : JNR
BACKGROUND: Surgery-related pressure injury (SRPI) is a serious problem in patients who undergo cardiovascular surgery. Identifying patients at a high risk of SRPI is important for clinicians to recognize and prevent it expeditiously. Machine learnin...

Supervised machine learning-based prediction for in-hospital pressure injury development using electronic health records: A retrospective observational cohort study in a university hospital in Japan.

International journal of nursing studies
BACKGROUND: In hospitals, nurses are responsible for pressure injury risk assessment using several kinds of risk assessment scales. However, their predictive validity is insufficient to initiate targeted preventive strategy for each patient. The use ...

Predicting pressure injury using nursing assessment phenotypes and machine learning methods.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Pressure injuries are common and serious complications for hospitalized patients. The pressure injury rate is an important patient safety metric and an indicator of the quality of nursing care. Timely and accurate prediction of pressure in...

Exploring a Fuzzy Rule Inferred ConvLSTM for Discovering and Adjusting the Optimal Posture of Patients with a Smart Medical Bed.

International journal of environmental research and public health
Several countries nowadays are facing a tough social challenge caused by the aging population. This public health issue continues to impose strain on clinical healthcare, such as the need to prevent terminal patients' pressure ulcers. Provocative app...

Modeling and prediction of pressure injury in hospitalized patients using artificial intelligence.

BMC medical informatics and decision making
BACKGROUND: Hospital-acquired pressure injuries (PIs) induce significant patient suffering, inflate healthcare costs, and increase clinical co-morbidities. PIs are mostly due to bed-immobility, sensory impairment, bed positioning, and length of hospi...

Deep learning approach based on superpixel segmentation assisted labeling for automatic pressure ulcer diagnosis.

PloS one
A pressure ulcer is an injury of the skin and underlying tissues adjacent to a bony eminence. Patients who suffer from this disease may have difficulty accessing medical care. Recently, the COVID-19 pandemic has exacerbated this situation. Automatic ...

Development and validation of a machine learning algorithm-based risk prediction model of pressure injury in the intensive care unit.

International wound journal
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 clinical data. A retrospective cohort study design was employed to develop and ...

Pressure Injury Prediction Model Using Advanced Analytics for At-Risk Hospitalized Patients.

Journal of patient safety
OBJECTIVE: Analyzing pressure injury (PI) risk factors is complex because of multiplicity of associated factors and the multidimensional nature of this injury. The main objective of this study was to identify patients at risk of developing PI.