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An Interpretable Machine Learning Approach to Estimate the Influence of Inflammation Biomarkers on Cardiovascular Risk Assessment by M. Roseiro; J. Henriques; S. Paredes; T. Rocha; J. Sousa is a Medicine article available to read on EtoBox.

What is An Interpretable Machine Learning Approach to Estimate the Influence of Inflammation Biomarkers on Cardiovascular Risk Assessment about?

Background and objective: Cardiovascular disease has a huge impact on health care services, originating unsustainable costs at clinical, social, and economic levels. In this context, patients' risk stratification tools are central to support clinical decisions contributing to the implementation of effective preventive health care. Although useful, these tools present some limitations, in particular, some lack of performance as well as the impossibility to consider new risk factors potentially important in the prognosis of severe cardiac events. Moreover, the actual use of these tools in the daily practice requires the physicians' trust. The main goal of this work addresses these two issues: (i) evaluate the importance of inflammation biomarkers when combined with a risk assessment tool; (ii) incorporation of personalization and interpretability as key elements of that assessment. Methods: Firstly, machine learning based models were created to assess the potential of the inflammation biomarkers applied in secondary prevention, namely in the prediction of the six month risk of death/myocardial infarction. Then, an approach based on three main phases was created: (i) set of interpreta

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Author

M. Roseiro; J. Henriques; S. Paredes; T. Rocha; J. Sousa

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Medicine (Physical Sciences)