Predictive modeling of gestational weight gain: a machine learning multiclass classification study.

Background: Gestational weight gain (GWG) is a critical factor influencing maternal and fetal health. Excessive or insufficient GWG can lead to various complications, including gestational diabetes, hypertension, cesarean delivery, low birth weight, and preterm birth. This study aims to develop and evaluate machine learning models to predict GWG categories: below, within, or above recommended guidelines.

Modifiable risk factors and excess mortality in depression: Data from the UK Biobank.

Objective: To study the role of modifiable risk factors in explaining the excess mortality associated with depression using data from the UK Biobank, a middle-aged and elderly cohort recruited in 2006-2010.

Methods: We estimated the prevalence and relative mortality associated with modifiable risk factors and groups of risk factors (socioeconomic factors, diet and exercise, smoking and substance-related disorders, and cardiometabolic diseases) in a subsample of probable cases of lifetime/current depression (n = 51,302) versus non-cases. We also estimated the relative mortality associated with depression and the percentages of excess mortality associated with depression explained by modifiable risk factors in the total sample (499,762).

Fairness of Machine Learning Algorithms for Predicting Foregone Preventive Dental Care for Adults.

Importance: Access to routine dental care prevents advanced dental disease and improves oral and overall health. Identifying individuals at risk of foregoing preventive dental care can direct prevention efforts toward high-risk populations.

Objective: To predict foregone preventive dental care among adults overall and in sociodemographic subgroups and to assess the algorithmic fairness.

Spatial Clusters of Cancer Mortality in Brazil: A Machine Learning Modeling Approach.

Our aim was to test if machine learning algorithms can predict cancer mortality (CM) at an ecological level and use these results to identify statistically significant spatial clusters of excess cancer mortality (eCM). Age-standardized CM was extracted from the official databases of Brazil. Predictive features included sociodemographic and health coverage variables.

The Impact of Time, Region, and Income Level on Stillbirth and Neonatal Mortality in Brazil, 2000-2019.

Objective: To describe trends in perinatal loss across Brazil, a country that transitioned in 2006 from a lower-middle income to an upper-middle income country, from 2000 to 2019 and analyze the effect of municipal wealth status on perinatal outcomes.

Study Design: We conducted an ecological cohort study, based on publicly available data from the Brazilian Ministry of Health's data repository on live births and deaths. The Atlas of Human Development in Brazil was used to associate each region with a World Bank income classification.

Emergency department use and Artificial Intelligence in Pelotas: design and baseline results.

Objetivo: To describe the initial baseline results of a population-based study, as well as a protocol in order to evaluate the performance of different machine learning algorithms with the objective of predicting the demand for urgent and emergency services in a representative sample of adults from the urban area of Pelotas, Southern Brazil.

Methods: The study is entitled "Emergency department use and Artificial Intelligence in PELOTAS (RS) (EAI PELOTAS)" (https://wp.ufpel.

A Machine Learning Prediction Model for Immediate Graft Function After Deceased Donor Kidney Transplantation.

Background: After kidney transplantation (KTx), the graft can evolve from excellent immediate graft function (IGF) to total absence of function requiring dialysis. Recipients with IGF do not seem to benefit from using machine perfusion, an expensive procedure, in the long term when compared with cold storage. This study proposes to develop a prediction model for IGF in KTx deceased donor patients using machine learning algorithms.

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.

Clusters of Pregnant Women with Severe Acute Respiratory Syndrome Due to COVID-19: An Unsupervised Learning Approach.

COVID-19 has been widely explored in relation to its symptoms, outcomes, and risk profiles for the severe form of the disease. Our aim was to identify clusters of pregnant and postpartum women with severe acute respiratory syndrome (SARS) due to COVID-19 by analyzing data available in the Influenza Epidemiological Surveillance Information System of Brazil (SIVEP-Gripe) between March 2020 and August 2021. The study's population comprised 16,409 women aged between 10 and 49 years old.

Functional mobility and 10-year all-cause and cause-specific mortality in older people from São Paulo, Brazil.

Background: A better understanding of performance in functional mobility tasks related to the mortality patterns for the different causes of death for the Brazilian older population is still a challenge.

Objective: To analyze if gait speed and chair stand test performance are associated with mortality in older adults, and if the overall mobility status changes the effect of other mortality risk factors.

Methods: The data were from SABE (Health, Well-being and Aging Study), a multiple-cohort study conducted in São Paulo, Brazil, with a representative sample of people aged 60 and more.

Machine Learning for Hypertension Prediction: a Systematic Review.

Purpose Of Review: To provide an overview of the literature regarding the use of machine learning algorithms to predict hypertension. A systematic review was performed to select recent articles on the subject.

Recent Findings: The screening of the articles was conducted using a machine learning algorithm (ASReview).

Mortality, survival, and causes of death in mental disorders: comprehensive prospective analyses of the UK Biobank cohort.

Background: The UK Biobank is a large middle-aged cohort recruited in 2006-2010. We used data from its participants to analyze mortality, survival, and causes of death associated with mental disorders.

Methods: Our exposures were mental disorders identified using (1) symptom-based outcomes derived from an online Mental Health Questionnaire ( = 157 329), including lifetime/current depression, lifetime/current generalized anxiety disorder, lifetime/recent psychotic experience, lifetime bipolar disorder, current alcohol use disorder, and current posttraumatic stress disorder and (2) hospital data linkage of diagnoses within the International Classification of Diseases, 10th revision (ICD-10) ( = 502 422), including (A) selected diagnoses or groups of diagnoses corresponding to symptom-based outcomes and (B) all psychiatric diagnoses, grouped by ICD-10 section.

Early identification of older individuals at risk of mobility decline with machine learning.

Background: The early identification of individuals at risk of mobility decline can improve targeted strategies of prevention.

Aims: To evaluate the predictive performance of machine learning (ML) algorithms in identifying older individuals at risk of future mobility decline.

Methods: We used data from the SABE Study (Health, Well-being and Aging Study), a representative sample of people aged 60 years and more, living in the Municipality of São Paulo, Brazil.

Machine learning analysis to predict health outcomes among emergency department users in Southern Brazil: a protocol study.

Objective: Emergency services are essential to the organization of the health care system. Nevertheless, they face different operational difficulties, including overcrowded services, largely explained by their inappropriate use and the repeated visits from users. Although a known situation, information on the theme is scarce in Brazil, particularly regarding longitudinal user monitoring.

Neonatal mortality prediction with routinely collected data: a machine learning approach.

Background: Recent decreases in neonatal mortality have been slower than expected for most countries. This study aims to predict the risk of neonatal mortality using only data routinely available from birth records in the largest city of the Americas.

Methods: A probabilistic linkage of every birth record occurring in the municipality of São Paulo, Brazil, between 2012 e 2017 was performed with the death records from 2012 to 2018 (1,202,843 births and 447,687 deaths), and a total of 7282 neonatal deaths were identified (a neonatal mortality rate of 6.

Predictors of tooth loss: A machine learning approach.

Introduction: Little is understood about the socioeconomic predictors of tooth loss, a condition that can negatively impact individual's quality of life. The goal of this study is to develop a machine-learning algorithm to predict complete and incremental tooth loss among adults and to compare the predictive performance of these models.

Methods: We used data from the National Health and Nutrition Examination Survey from 2011 to 2014.

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.

Cause-specific mortality prediction in older residents of São Paulo, Brazil: a machine learning approach.

Background: Populational ageing has been increasing in a remarkable rate in developing countries. In this scenario, preventive strategies could help to decrease the burden of higher demands for healthcare services. Machine learning algorithms have been increasingly applied for identifying priority candidates for preventive actions, presenting a better predictive performance than traditional parsimonious models.

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.

[Quality of diet and daily spending on food by adults with cardiovascular disease in Brazil].

Adherence to a healthy diet depends on factors such as food prices, while studies in developed countries have identified higher costs of more nutritional foods. The current study aimed to assess the direct food expenditures by adults with cardiovascular disease in Brazil, investigating the relationship between cost and quality of diet. The study used data from a randomized clinical trial, the BALANCE Program.

Basic sanitation and self-reported health in Brazilian capitals: a multilevel analysis.

Objective: This study aimed to analyze the association between the contextual determinants related to basic sanitation and self-reported health in Brazilian capitals.

Methods: The sample consisted of 27,017 adults (≥18 years) residing in the 27 Brazilian capitals in 2013, from the National Health Survey (PNS). The association between self-reported health and sanitation (sewage system, water supply and garbage collection) was analyzed using Bayesian multilevel models, controlling for individual factors (first level of the model) and area-level socioeconomic characteristics (second level).

Income Inequality and Self-Reported Health Among Older Adults in Brazil.

This study analyzes the association between income inequality and self-reported health (SRH) in older adults, and separately for the young-old and very-old groups, residing in each of the 27 Brazilian capitals. The sample consisted of 4,912 individuals aged 60 or older residing in Brazilian capitals in 2013. Bayesian multilevel models were applied to the whole sample and separately for individuals aged 60 to 79 (young-old), and 80 or more (very-old).

Blurred lines: racial misclassification in death certificates in Brazil.

Objectives: To analyze the agreement between self-reported race and race reported on death certificates for older (≥ 60 years) residents of São Paulo, Brazil (from 2000 to 2016) and to estimate weights to correct mortality data by race.

Methods: We used data from the Health, Well-Being and Aging Study (SABE) and from Brazil's Mortality Information System. Misclassification was identified by comparing individual self-reported race with the corresponding race on the death certificate (n = 1012).