Multicenter comparative analysis of local and aggregated data training strategies in COVID-19 outcome prediction with Machine learning.

Machine learning (ML) is a promising tool in assisting clinical decision-making for improving diagnosis and prognosis, especially in developing regions. It is often used with large samples, aggregating data from different regions and hospitals. However, it is unclear how this affects predictions in local centers.

Big data evidence of the impact of COVID-19 hospitalizations on mortality rates of non-COVID-19 critically ill patients.

The COVID-19 virus caused a global pandemic leading to a swift policy response. While this response was designed to prevent the spread of the virus and support those with COVID-19, there is growing evidence regarding measurable impacts on non-COVID-19 patients. The paper uses a large dataset from administrative records of the Brazilian public health system (SUS) to estimate pandemic spillover effects in critically ill health care delivery, i.

Using machine learning to estimate health spillover effects.

We develop a nonparametric model to study health spillover effects of policy interventions. We use double/debiased machine learning to estimate the model using data from 74 hospitals in Rio de Janeiro, Brazil, and examine cross-patient spillover effects during the COVID-19 pandemic. The pandemic forced hospitals to develop new protocols to offer intensive care to both COVID and non-COVID patients.

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.

Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study.

Artificial intelligence (AI) algorithms are transforming several areas of the digital world and are increasingly being applied in healthcare. Mobile apps based on predictive machine learning models have the potential to improve health outcomes, but there is still no consensus on how to inform doctors about their results. The aim of this study was to investigate how healthcare professionals prefer to receive predictions generated by machine learning algorithms.

COVID-19 and Indigenous health in the Brazilian Amazon.

We test whether the COVID-19 pandemic has an ethnicity-differentiated (Indigenous vs non-Indigenous) effect on infant health in the Brazilian Amazon. Using vital statistics data we find that Indigenous infants born during the pandemic are 0.5% more likely to have very low birth weights.

Network analysis as an alternative way to interpret constitutions.

This article aims to identify networks of constitutional text structured in an imperceptible way. Constitutions has the power to reveal how its devices "dialogue" with each other, forming their own communicative communities. With the help from the network analysis methodology, an interpretative model of textual mapping is proposed based on internal references between the provisions of one Constitution.