scholarly journals Machine Learning for Neonatal Mortality Risk Assessment: A Case Study Using Public Health Data from São Paulo

2020 ◽  
Author(s):  
Carlos Eduardo Beluzo ◽  
Luciana Correia Alves ◽  
Rodrigo Bresan ◽  
Natália Arruda ◽  
Ricardo Sovat ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Risk Assessment ◽  
Network Architecture ◽  
Health Data ◽  
Sao Paulo ◽  
São Paulo ◽  
Public Health Data ◽  
Tools And Techniques

AbstractInfant mortality is a reflection of a complex combination of biological, socioeconomic and health care factors that require various data sources for a thorough analysis. Consequently, the use of specialized tools and techniques to deal with a large volume of data is extremely helpful. Machine learning has been applied to solve problems from many domains and presents great potential for the proposed problem, which would be an innovation in Brazilian reality. In this paper, an innovative method is proposed to perform a neonatal death risk assessment using computer vision techniques. Using mother, pregnancy care and child at birth features, from a dataset containing neonatal samples from São Paulo city public health data, the proposed method encodes images features and uses a custom convolutional neural network architecture to classification. Experiments show that the method is able to detect death samples with accuracy of 90.61%.

Environmental and public health effects of vehicle emissions in a large metropolis: Case study of a truck driver strike in Sao Paulo, Brazil

2020 ◽  
Vol 11 (6) ◽  
pp. 24-31 ◽  
Author(s):  
Luciana Ferreira Leite Leirião ◽  
Daniela Debone ◽  
Theotonio Pauliquevis ◽  
Nilton Manuel Évora do Rosário ◽  
Simone Georges El Khouri Miraglia
Keyword(s):  
Public Health ◽  
Health Effects ◽  
Vehicle Emissions ◽  
Sao Paulo ◽  
Truck Driver ◽  
São Paulo

scholarly journals Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine-Learning

2020 ◽  
Author(s):  
Jeany Delafiori ◽  
Luiz Claudio Navarro ◽  
Rinaldo Focaccia Siciliano ◽  
Gisely Cardoso de Melo ◽  
Estela Natacha Brandt Busanello ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Financial Burden ◽  
Cost Effective ◽  
Sao Paulo ◽  
São Paulo ◽  
Real World Application ◽  
Patient Screening ◽  
Health Related ◽  
Resources Planning

COVID-19 is still placing a heavy health and financial burden worldwide. Impairments in patient screening and risk management play a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with instrumental analysis using mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cohort of 728 patients (369 confirmed COVID-19 and 359 controls) was enrolled from three Brazilian epicentres (Sao Paulo capital, Sao Paulo countryside and Manaus) in the months of April, May, June and July 2020. We were able to elect and identify 21 molecules that are related to the disease's pathophysiology and 26 features to patient's health-related outcomes. With specificity >97% and sensitivity >83% from blinded data, this screening approach is understood as a tool with great potential for real-world application.

scholarly journals A public health risk assessment for yellow fever vaccination: a model exemplified by an outbreak in the state of São Paulo, Brazil

2015 ◽  
Vol 110 (2) ◽  
pp. 230-234 ◽  
Author(s):  
Ana Freitas Ribeiro ◽  
Ciléa Tengan ◽  
Helena Keico Sato ◽  
Roberta Spinola ◽  
Melissa Mascheretti ◽  
...  
Keyword(s):  
Public Health ◽  
Risk Assessment ◽  
Health Risk ◽  
Yellow Fever ◽  
Health Risk Assessment ◽  
Sao Paulo ◽  
The State ◽  
São Paulo ◽  
Public Health Risk ◽  
Yellow Fever Vaccination

scholarly journals Machine Learning to Predict Neonatal Mortality Using Public Health Data from São Paulo - Brazil

2020 ◽  
Author(s):  
Carlos Eduardo Beluzo ◽  
Luciana Correia Alves ◽  
Everton Silva ◽  
Rodrigo Bresan ◽  
Natália Arruda ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Infant Mortality ◽  
Neonatal Mortality ◽  
Sao Paulo ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
São Paulo ◽  
Health Quality ◽  
Health Features

AbstractInfant mortality is one of the most important socioeconomic and health quality indicators in the world. In Brazil, neonatal mortality accounts to 70% of the infant mortality. Despite its importance, neonatal mortality shows increasing signals, which causes concerns about the necessity of efficient and effective methods able to help reducing it. In this paper a new approach is proposed to classify newborns that may be susceptible to neonatal mortality by applying supervised machine learning methods on public health features. The approach is evaluated in a sample of 15,858 records extracted from SPNeoDeath dataset, which were created on this paper, from SINASC and SIM databases from São Paulo city (Brazil) for this paper intent. As a results an average AUC of 0.96 was achieved in classifying samples as susceptible to death or not with SVM, XGBoost, Logistic Regression and Random Forests machine learning algorithms. Furthermore the SHAP method was used to understand the features that mostly influenced the algorithms output.

scholarly journals IoTs, Machine Learning (ML), AI and Digital Transformation Affects Various Industries - Principles and Cybersecurity Risks Solutions

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 501-513
Author(s):  
Nguyen Dinh Trung ◽  
Dinh Tran Ngoc Huy ◽  
Trung-Hieu Le
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Data ◽  
Digital Transformation ◽  
Development Path ◽  
Public Health Data ◽  
Infrared Rays ◽  
Manufacturing Sectors ◽  
Applications Of Machine Learning

Our purpose to conduct this research is that we would like to present advantages and applications of internet of things (IoTs), Machine learning (ML), AI - Artificial intelligence and digital transformation in Education, Medicine-hospitals, Tourism and Manufacturing Sectors. In this paper authors will use methods such as empirical research and practices and experiences in infrared rays system applications in emerging markets such as Vietnam. Research Results find out that in education sector, ML and IoTs and AI has affected methods of teaching and methods of evaluating students in classroom and from then, teachers or instructors can decide suitable career development path for learners. Last but not least, ML and IoTs and AI together also has certain impacts in hospitals and medicine sector where public health data and patients information and diseases information are recorded and processed faster with Big Data. Till the end, we have enough information to propose implications for future researches on applications of machine learning in each specific sector and also, cybersecurity Risk management also need for implementing and applying ML and IoTs and AI.

Water quality and risk assessment of dug wells: a case study for a poor community in the city of São Paulo, Brazil

2012 ◽  
Vol 68 (3) ◽  
pp. 899-910 ◽  
Author(s):  
Alexandra V. Suhogusoff ◽  
Ricardo Hirata ◽  
Luiz Carlos K. M. Ferrari
Keyword(s):  
Water Quality ◽  
Risk Assessment ◽  
Sao Paulo ◽  
São Paulo ◽  
Dug Wells ◽  
The City

scholarly journals The differential impact of economic recessions on health systems in middle-income settings: a comparative case study of unequal states in Brazil

2020 ◽  
Vol 5 (2) ◽  
pp. e002122 ◽  
Author(s):  
Lucas Salvador Andrietta ◽  
Maria Luiza Levi ◽  
Mário C Scheffer ◽  
Maria Teresa Seabra Soares de Britt Alves ◽  
Bruno Luciano Carneiro Alves de Oliveira ◽  
...  
Keyword(s):  
Public Health ◽  
Health Systems ◽  
System Performance ◽  
Health Financing ◽  
Sao Paulo ◽  
Comparative Case Study ◽  
São Paulo ◽  
Middle Income ◽  
Economic Recessions

IntroductionAlthough economic crises are common in low/middle-income countries (LMICs), the evidence of their impact on health systems is still scant. We conducted a comparative case study of Maranhão and São Paulo, two unevenly developed states in Brazil, to explore the health financing and system performance changes brought in by its 2014–2015 economic recession.MethodsDrawing from economic and health system research literature, we designed a conceptual framework exploring the links between macroeconomic factors, labour markets, demand and supply of health services and system performance. We used data from the National Health Accounts and National Household Sample Survey to examine changes in Brazil’s health spending over the 2010–2018 period. Data from the National Agency of Supplementary Health database and the public health budget information system were employed to compare and contrast health financing and system performance of São Paulo and Maranhão.ResultsOur analysis shows that Brazil’s macroeconomic conditions deteriorated across the board after 2015–2016, with São Paulo’s economy experiencing a wider setback than Maranhão’s. We showed how public health expenditures flattened, while private health insurance expenditures increased due to the recession. Public financing patterns differed across the two states, as health funding in Maranhão continued to grow after the crisis years, as it was propped up by transfers to local governments. While public sector staff and beds per capita in Maranhão were not affected by the crisis, a decrease in public physicians was observed in São Paulo.ConclusionOur case study suggests that in a complex heterogeneous system, economic recessions reverberate unequally across its parts, as the effects are mediated by private spending, structure of the market and adjustments in public financing. Policies aimed at mitigating the effects of recessions in LMICs will need to take such differences into account.

Sign in / Sign up

Export Citation Format

Share Document