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- Innovation and knowledge transfer for monitoring, predicting and preventing ulcers: the Sensomatt ApproachPublication . Silva, Arlindo F.; Santos, Osvaldo; Ribeiro, Fernando Reinaldo; Fidalgo, Filipe; Metrôlho, J.C.M.M.; Amini, Mohammad; Fonseca, Luís Filipe Rodrigues; Dionísio, Rogério PaisPressure ulcers are skin injuries that develop mainly over bony areas as the result of prolonged pressure caused by the immo- bility of bedridden patients. They constitute not only a source of additional suffering for these patients but also contribute to the burnout of healthcare professionals who must maintain continuous monitoring of these patients. Data from countries such as the UK or the USA allows the cost of this problem to be estimated to be, respectively, near £2 billion and $80 billion. In this article, we describe the SensoMatt approach to pressure ulcer prevention and management, which is being developed as a research project that includes partners from industry, healthcare, and academia. The SensoMatt solution is centered on a pressure sheet that is placed under the patient’s mattress, complemented by an online management portal and a mobile app. These provide patients and healthcare providers with an unparalleled set of services that include personalized analysis, prevention warnings and recommendations.
- 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