Machine learning tools in chronic disease management: scoping review
DOI:
https://doi.org/10.37914/riis.v7i1.359Keywords:
Machine Learning, Chronic Disease, NursingAbstract
Background: the implementation of technologies based on Artificial Intelligence (AI) in the health sector, in particular machine learning (ML), has had a significant transformational effect. Their use improves disease prediction, classification and diagnosis, benefiting both users and healthcare professionals. Objective: to map ML tools for chronic disease management, with relevance to nursing care for people with chronic diseases. Methodology: scoping review based on the recommendations of the Joanna Briggs Institute. The MEDLINE Complete via PUBMED, CINAHL Complete via EBSCO, SCOPUS, OpenGrey, RCAAP and DART-Europe databases were used, with no time limit. Results: seven articles were included and 9 AI tools associated with chronic disease management were identified, namely chronic kidney disease, chronic obstructive pulmonary disease, hepatitis C, heart failure and chronic venous insufficiency. Conclusion: the tools identified have the potential to contribute to improving nursing care, particularly in identifying risk factors associated with chronic diseases, detecting exacerbations early, continuously monitoring and evaluating the effectiveness of treatment and supporting clinical decision-making.
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Copyright (c) 2023 Igor Soares-Pinto, Marta Sofia Ferreira Sá, Ana Margarida Martins Bastos Alves, Maria Teresa Barbosa Pinto Sousa, Ana Vanessa Fernandes Carvalho, Cátia Moreira
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