Literature review of machine-learning algorithms for pressure ulcer prevention: challenges and opportunities

article

Ribeiro, Fernando Reinaldo
Fidalgo, Filipe
Silva, Arlindo
Metrôlho, J.C.M.M.
Santos, Osvaldo
Dionísio, Rogério

MDPI

Ribeiro, Fernando, Filipe Fidalgo, Arlindo Silva, José Metrôlho, Osvaldo Santos, and Rogério Dionisio. 2021. “Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities.” Informatics 8(4).

2227-9709

Literature review of machine-learning algorithms for pressure ulcer prevention: challenges and opportunities

Artificial intelligence
Burnout
Clinical decision support
Literature review
Machine learning
Pressure injury prevention
Pressure ulcers prevention
Quality of healthcare

CENTRO-01-0247-FEDER-070107

2021-12-03T16:56:45Z
2021-12-03T16:56:45Z
2021

Pressure ulcers are associated with significant morbidity, resulting in a decreased quality of life for the patient, and contributing to healthcare professional burnout, as well as an increase of health service costs. Their prompt diagnosis and treatment are important, and several studies have proposed solutions to help healthcare professionals in this process. This work analyzes studies that use machine-learning algorithms for risk assessment and management of preventive treatments for pressure ulcers. More specifically, it focuses on the use of machine-learning algorithms that combine information from intrinsic and extrinsic pressure-ulcer predisposing factors to produce recommendations/alerts to healthcare professionals. The review includes articles published from January 2010 to June 2021. From 60 records screened, seven articles were analyzed in full-text form. The results show that most of the proposed algorithms do not use information related to both intrinsic and extrinsic predisposing factors and that many of the approaches separately address one of the following three components: data acquisition; data analysis, and production of complementary support to well-informed clinical decision-making. Additionally, only a few studies describe in detail the outputs of the algorithm, such as alerts and recommendations, without assessing their impacts on healthcare professionals’ activities
info:eu-repo/semantics/publishedVersion

openAccess
http://creativecommons.org/licenses/by/4.0/

eng