Associations between obesity and severity of coronavirus disease 2019 in Brazilian inpatients: A 2021 secondary data analysis.

In the backdrop of the global obesity pandemic, recognized as a notable risk factor for coronavirus disease 2019 (COVID-19) complications, the study aims to explore clinical and epidemiological attributes of hospitalized COVID-19 patients throughout 2021 in Brazil. Focused on four distinct age cohorts, the investigation scrutinizes parameters such as intensive care unit (ICU) admission frequency, invasive mechanical ventilation (IMV) usage, and in-hospital mortality among individuals with and without obesity. Using a comprehensive cross-sectional study methodology, encompassing adult COVID-19 cases, data sourced from the Influenza Epidemiological Surveillance Information System comprises 329 206 hospitalized patients.

Predicting the Need for Blood Transfusions in Cardiac Surgery: A Comparison between Machine Learning Algorithms and Established Risk Scores in the Brazilian Population.

Introduction: Blood transfusion is a common practice in cardiac surgery, despite its well-known negative effects. To mitigate blood transfusion-associated risks, identifying patients who are at higher risk of needing this procedure is crucial. Widely used risk scores to predict the need for blood transfusions have yielded unsatisfactory results when validated for the Brazilian population.

Developing Machine Learning Algorithms for the Prediction of Early Death in Elderly Cancer Patients: Usability Study.

Background: The importance of classifying cancer patients into high- or low-risk groups has led many research teams, from the biomedical and bioinformatics fields, to study the application of machine learning (ML) algorithms. The International Society of Geriatric Oncology recommends the use of the comprehensive geriatric assessment (CGA), a multidisciplinary tool to evaluate health domains, for the follow-up of elderly cancer patients. However, no applications of ML have been proposed using CGA to classify elderly cancer patients.