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- Integrated system for pressure ulcers monitoring and preventionPublication . Fonseca, Luís Filipe Rodrigues; Ribeiro, Fernando Reinaldo; Metrôlho, J.C.M.M.; Fidalgo, Filipe; Dionísio, Rogério Pais; Silva, Arlindo F.; Santos, Osvaldo; Amini, MohammadPressure ulcers are a critical issue for patients and healthcare professionals, requiring their frequent monitoring, with a consequent impact on healthcare costs. This problem has been gaining attention and approaches have been proposed, using sensor-based systems, to facilitate this monitoring and help health caregivers to achieve greater effectiveness in the treatment of this type of ulcer. In this paper, the architecture, and the prototype of a new system for pressure ulcer monitoring and prevention are presented. It considers information related to both intrinsic and extrinsic predisposing factors and it addresses the components of data acquisition, data analysis, and production of complementary support to well-informed clinical decision-making. The system includes a pressure ulcer management portal and a mobile application, that allows caregivers to manage clinical information about pressure ulcers of the patients and uses data acquired from a pressure sensor sheet under the mattress to provide useful information for monitoring the patients. Considering the situation of each patient, the system will produce indicators/alerts to healthcare professionals, simultaneously improving pressure-ulcer patient care quality and safety and minimizing the burnout in healthcare professionals.
- Literature review of machine-learning algorithms for pressure ulcer prevention: challenges and opportunitiesPublication . Ribeiro, Fernando Reinaldo; Fidalgo, Filipe; Silva, Arlindo F.; Metrôlho, J.C.M.M.; Santos, Osvaldo; Dionísio, RogérioPressure 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