RESUMO
Aim: This systematic review aims to evaluate the current body of research surrounding the efficacy of artificial intelligence (AI) in cardiac rehabilitation. Presently, AI can be incorporated into personal devices such as smart watches and smartphones, in diagnostic and home monitoring devices, as well as in certain inpatient care settings. Materials & methods: The PRISMA guidelines were followed in this review. Inclusion and exclusion criteria were set using the Population, Intervention, Comparison and Outcomes (PICO) tool. Results: Eight studies meeting the inclusion criteria were found. Conclusion: Incorporation of AI into healthcare, cardiac rehabilitation delivery, and monitoring holds great potential for early detection of cardiac events, allowing for home-based monitoring, and improved clinician decision making.
Lay abstract Artificial intelligence (AI) involves the use of technologies capable of making decisions based on data provided. AI can be used in healthcare to provide actionable data for a clinician by analyzing patterns in patient data to predict outcomes and guide treatment. Cardiovascular disease is the leading cause of death worldwide. Cardiac rehabilitation is a therapy proven to reduce mortality and morbidity from cardiovascular disease. This study outlines three cases of AI based healthcare tools in cardiac rehabilitation. This includes the provision of personalized, home-based cardiac rehabilitation, the early detection of cardiac events through smart watch monitoring and by providing clinician decision making support in cardiac failure rehabilitation.
Assuntos
Inteligência Artificial , Reabilitação Cardíaca , Atenção à Saúde , Hospitalização , HumanosRESUMO
BACKGROUND: As a result of the high contagiousness and transmissibility of SARS-CoV-2, studying the location of the case clusters that will follow, will help understand the risk factors related to the disease transmission. In this study, we aim to identify the transmission cluster category and settings that can guide decision-makers which areas to be opened again. METHODS: A thorough review of the literature and the media articles were performed. After data verification, we included cluster data from eight countries as of 16th May 2020. Clusters were further categorized into 10 categories and analysis was performed. The data was organized and presented in an easily accessible online sheet. RESULTS: Among the eight included countries, we have found 3905 clusters and a total number of 1,907,944 patients. Indoor settings (mass accommodation and residential facilities) comprised the highest number of both number of clusters (3315/3905) and infected patients (1,837,019/1,907,944), while the outdoor ones comprised 590 clusters and 70,925 patients. Mass accommodation was associated with the highest number of cases in 5 of the 7 countries with data available. Social events and residential settings were responsible for the highest number of cases in the two remaining countries. In the USA, workplace facilities have reported 165 clusters of infection including 122 food production facilities. CONCLUSIONS: Lockdown could truly be a huge burden on a country's economy. However, with the proper knowledge concerning the transmissibility and the behaviour of the disease, better decisions could be made to guide the appropriate removal of lockdown across the different fields and regions.
