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Predicting cardiovascular events using machine learning

Grant number: 21/06137-4
Support Opportunities:Regular Research Grants
Duration: April 01, 2022 - March 31, 2025
Field of knowledge:Engineering - Biomedical Engineering - Medical Engineering
Convênio/Acordo: European Commission (Horizon 2020)
Principal Investigator:Paulo Mazzoncini de Azevedo Marques
Grantee:Paulo Mazzoncini de Azevedo Marques
Principal researcher abroad: Peter Rainer
Institution abroad: University of Graz, Austria
Host Institution: Faculdade de Medicina de Ribeirão Preto (FMRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
Associated researchers:André Schmidt ; Elen Almeida Romão ; João Mazzoncini de Azevedo Marques ; José Abrão Cardeal da Costa ; Sandro Scarpelini

Abstract

Cardiovascular disease is the leading cause of death worldwide. Underlying atherosclerosis and ensuing conditions such as myocardial infarction, ischemic heart disease and stroke cause tremendous morbidity, mortality, and economic loss. Early identification of patients with high risk for such clinical events enables preventive actions. The use of machine learning (ML) for risk prediction can outperform traditional risk scores. Although many ML models have been developed over the last years, validation is rare. We do not know how models perform in different clinical settings or populations. Furthermore, using numerous predictors, it is hard to transfer models to other health systems. Recently, we developed risk prediction models for major adverse cardiovascular events and progression of kidney disease. However, the models still lack external validation, hindering implementation in different clinical contexts and limiting generalizability. As such, this project has three main aims. The first is to validate and improve our ML models across different hospital networks and populations. The second aim is to integrate the ML models in different hospital information systems and evaluate their impact on daily hospital routine. Finally, building upon these validated models, the third aim addresses effective risk communication strategies in order to effect behavioral changes in patients. Therefore, our project makes a fundamental contribution towards employing innovative personalized risk prediction and medicine and assess its clinical implementation in a transnational context. (AU)

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