Improving the performance of machine learning algorithms for health outcomes predictions in multicentric cohorts.

Machine learning algorithms are being increasingly used in healthcare settings but their generalizability between different regions is still unknown. This study aims to identify the strategy that maximizes the predictive performance of identifying the risk of death by COVID-19 in different regions of a large and unequal country. This is a multicenter cohort study with data collected from patients with a positive RT-PCR test for COVID-19 from March to August 2020 (n = 8477) in 18 hospitals, covering all five Brazilian regions.

Correction: Saito et al. Sex-Specific Mortality from Asbestos-Related Diseases, Lung and Ovarian Cancer in Municipalities with High Asbestos Consumption, Brazil, 2000-2017. 2022, , 3656.

In the original publication [...

Sex-Specific Mortality from Asbestos-Related Diseases, Lung and Ovarian Cancer in Municipalities with High Asbestos Consumption, Brazil, 2000-2017.

The aim of this study is to compare the mortality rates for typical asbestos-related diseases (ARD-T: mesothelioma, asbestosis, and pleural plaques) and for lung and ovarian cancer in Brazilian municipalities where asbestos mines and asbestos-cement plants had been operating (areas with high asbestos consumption, H-ASB) compared with in other municipalities. The death records for adults aged 30+ years were retrieved from multiple health information systems. In the 2000-2017 time period, age-standardized mortality rates (standard: Brazil 2010) and standardized rate ratios (SRR; H-ASB vs.

Prediction of absenteeism in public schools teachers with machine learning.

Objective: To predict the risk of absence from work due to morbidities of teachers working in early childhood education in the municipal public schools, using machine learning algorithms.

Methods: This is a cross-sectional study using secondary, public and anonymous data from the Relação Anual de Informações Sociais, selecting early childhood education teachers who worked in the municipal public schools of the state of São Paulo between 2014 and 2018 (n = 174,294). Data on the average number of students per class and number of inhabitants in the municipality were also linked.

Recovering records on cancer of the larynx from anonymous health information databases.

Objective: To develop a linkage algorithm to match anonymous death records of cancer of the larynx (ICD-10 C32X), retrieved from the Mortality Information System (SIM) and the Hospital Information System of the Brazilian Unified National Health System (SIH-SUS) in Brazil.

Methodology: Death records containing ICD-10 C32X codes were retrieved from SIM and SIH-SUS, limited to individuals aged 30 years and over, between 2002 and 2012, in the state of São Paulo. The databases were linked using a unique key identifier developed with sociodemographic data shared by both systems.

A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, Brazil.

The new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate prognostic assessment is necessary to improve survival of patients, especially in developing countries. This study proposes to predict the risk of developing critical conditions in COVID-19 patients by training multipurpose algorithms.