Study and application of machine learning techniques unsupervised in the analysis of undesirable outcomes in the treatment of Tuberculosis

• Grant number: 21/01961-0
• Support Opportunities: Scholarships in Brazil - Master
• Start date: May 01, 2021
• End date: April 30, 2023
• Field of knowledge: Health Sciences - Collective Health - Public Health
• Principal Investigator: Domingos Alves
• Grantee: Ana Clara de Andrade Mioto
• Host Institution: Faculdade de Medicina de Ribeirão Preto (FMRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
• Associated research grant: 20/01975-9 - Digital health for the End TB strategy: from linked data integration to a better evidence-based decision making, AP.ESCIENCE.R

Abstract

This project aims to apply data mining techniques and machine learning to TB treatment data. The application of all steps related to the knowledge discovery process in databases (KDD) can allow a greater understanding of the concepts and patterns in the TB domain. In particular, unsupervised machine learning (AM) algorithms will be applied. This analysis is expected to identify unknown patterns that may associate socio-demographic, clinical factors with the different outcomes in the treatment of tuberculosis, in particular abandonment, death and resistance. (AU)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

MIOTO, Ana Clara de Andrade. Study and applications of machine learning techniques in tuberculosis bad outcomes. 2023. Master's Dissertation - Universidade de São Paulo (USP). Escola de Engenharia de São Carlos (EESC/SBD) São Carlos.