Mariana Tavares Mozini

Universidade de São Paulo

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Ana Clara de Andrade Mioto

Universidade de São Paulo

Domingos Alves

Universidade de São Paulo

Renan Barbieri Segamarchi

Universidade de São Paulo

Victor Cassão

Universidade de São Paulo

Newton Shydeo Brandão Miyoshi

Centro Universitário Barão de Mauá

Filipe Andrade Bernardi

Universidade de São Paulo

Vinícius Lima

Universidade de São Paulo

Afrânio Lineu Kritski

Universidade Federal do Rio de Janeiro

Giovane Thomazini Soares

Universidade de São Paulo

Francisco Barbosa-Júnior

Universidade de São Paulo

Cooperative Institutions

Universidade de São Paulo

Clinics Hospital of Ribeirão Preto

Universidade de Ribeirão Preto

Centro Universitário Barão de Mauá

Universidade Estadual de Campinas (UNICAMP)

Hospital Israelita Albert Einstein

Universidade Brasil

Fundação Oswaldo Cruz

Universidade Federal de Minas Gerais

Universidade Federal do Rio de Janeiro

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A Computational Infrastructure for Analyzing Tuberculosis Research Data in Brazil

Studies in health technology and informatics (2023)

Mariana Tavares Mozini Filipe Andrade Bernardi Ana Clara de Andrade Mioto Victor Cassão Afrânio Lineu Kritski

Tuberculosis (TB) is one of the infectious diseases that currently causes the most deaths, with 6.4 million new cases recorded in 2021. Although it is a curable disease, drug-resistant strains emerge due to a lack of hygiene and low-quality or inappropriate medications, among other factors. With this in mind, the World Health Organization initiated the End TB Strategy campaign to improve the health system in the fight against tuberculosis. For this, reliable and high-quality health data is necessary to create effective public policies. However, despite technological advancements such as emerging concepts like Big Data and the Internet of Things, generating health information faces several obstacles. Therefore, the present work aims to describe a pipeline for TB research in Brazil to contribute to obtaining high-quality data.

10.3233/shti230557

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Preliminary Results to Predict Tuberculosis Outcomes Applying Traditional and Automated Machine Learning Models

Procedia Computer Science (2023)

Ana Clara de Andrade Mioto Mariana Tavares Mozini Renan Barbieri Segamarchi Giovane Thomazini Soares Pedro Emilio Andrade Martins

Tuberculosis (TB) remains one of the most lethal infectious diseases in the world and, despite being preventable and curable, kills 4.500 people daily, according to the World Health Organization (WHO). Brazil, being a country heavily affected by TB, works to improve social intervention programs, since the decrease in the patients vulnerability seems to have a positive effect for the cure of TB. The Brazilian public health system records data on TB treatment that can guide actions and interventions. In this context, machine learning (ML) algorithms have been used successfully to analyze health and medicine (H&M) datasets. An emerging area of ML called Automated Machine Learning (Auto-ML) was tested in this analysis to predict the following TB results: good and bad outcomes. Our results indicate that it is possible to build reasonable ML models with the available data.

Vulnerability

10.1016/j.procs.2023.01.424

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OUTB: application for decision-support in the outcomes of Tuberculosis

Procedia Computer Science (2024)

Mariana Tavares Mozini R. Rothschild Ana Clara de Andrade Mioto Filipe Andrade Bernardi Vinícius Lima

Brazil is among the 30 countries with the highest tuberculosis prevalence, and many studies are trying to understand and fight the harmful outcomes of this disease. In that sense, the following project proposes the development of a mobile application prototype that centralizes diagnosis aid tools for tuberculosis, making the process more transparent for the patient, using the Single Patient Application (SPA) concept. The application was built based on JavaScript and the frameworks chosen were Vue Framework along with Framework7 due to its focus on interface components. Communication with the back-end was done through AJAX calls. As a result, a prototype was built with the five main screens that the user will interact with: authentication/login screen, home screen, list of available algorithms, outcomes algorithm home screen and patient history. The interface design is expected to facilitate the planning of other application areas and contribute to defining business rules. Once implemented on the server side, these rules can support an organized, patient-centered tuberculosis database.

Interface (matter)

10.1016/j.procs.2024.06.241

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Knowledge Discovery in Databases: Comorbidities in Tuberculosis Cases

Lecture notes in computer science (2022)

Isabelle Carvalho Mariane Barros Neiva Newton Shydeo Brandão Miyoshi Nathalia Yukie Crepaldi Filipe Andrade Bernardi

Relevance

10.1007/978-3-031-08757-8_1

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Exploring the Application of Actigraphy in Pregnancy: A Scoping Systematic Review

Thais G. Sardinha Mariana Tavares Mozini Francisco Barbosa-Júnior José Guilherme Cecatti Renato T. Souza

10.37766/inplasy2024.12.0066

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Unsupervised analysis of COVID-19 pandemic evolution in brazilian states: Vaccination Scenario

Procedia Computer Science (2023)

Victor Cassão Domingos Alves Ana Clara de Andrade Mioto Mariana Tavares Mozini Renan Barbieri Segamarchi

Brazil is one of the countries with the worst response against the pandemic scenario of coronavírus. At the beginning we were on average with 4000 deaths in a 24 hours period. In the course of this situation, large amounts of health and medicine datasets were being generated in real time, requiring effective ways to extract information and discover patterns that can help in the fight against this disease. And even more important is to monitor the progress of prophylactic measures and whether they are being effective in reducing the spread of the virus. Thus, the aim of this study is to analyze how the coronavirus has different ways to evolve in each Brazilian state with the influences of the vaccination process. To achieve this goal, the time series Clustering Technique based on a K-Means variation was applied, with the similarity metric Dynamic Time Warping (DTW). We produced this study using the data reported by the Ministry of Health in Brazil, referring to deaths per 100k inhabitants and all vaccination data available. Our results indicate an unevenly occurring vaccination and the need to identify other associated patterns with human development indices and other socio-economic indicators, being this the first analysis developed in the country, under the goals above.

Pandemic

Dynamic Time Warping

Christian ministry

10.1016/j.procs.2023.01.435

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