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| Author(s): |
Ana Clara de Andrade Mioto
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Escola de Engenharia de São Carlos (EESC/SBD) |
| Defense date: | 2023-11-01 |
| Examining board members: |
Domingos Alves;
Antonio Ruffino Netto
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| Advisor: | Domingos Alves |
| Abstract | |
Tuberculosis (TB) continues to be one of the deadliest infectious diseases globally, with millions of cases and deaths reported every year. This problem is exacerbated when associated with comorbidities such as HIV, making it even more lethal. Furthermore, socioeconomic and cultural factors play a significant role in the prevalence of TB, indicating a close link between the disease and poor social development. Brazil, significantly affected by Tuberculosis, has been working on actions and treatments that can be implemented for TB control and prevention, as well as reducing patient vulnerability. A crucial aspect for the implementation of these interventions is the availability of comprehensive health data and the application of data analysis techniques such as machine learning (ML) to improve the quality of care and medical decisions. In fact, studies have shown that ML is an emerging area in healthcare because it can learn from historical data and identify patterns that can help avoid unexpected outcomes in TB treatment, such as treatment abandonment, death, and drug resistance. In this context, this research aims to use knowledge discovery in databases (KDD) techniques and machine learning to analyze and identify unknown patterns that may relate sociodemographic and clinical factors to the likelihood of a certain negative outcome in TB treatment occurring with a patient. Furthermore, the increasing availability of patient data in the healthcare field makes the use of techniques like ML even more relevant to enhance the management of TB patients. (AU) | |
| FAPESP's process: | 21/01961-0 - Study and application of machine learning techniques unsupervised in the analysis of undesirable outcomes in the treatment of Tuberculosis |
| Grantee: | Ana Clara de Andrade Mioto |
| Support Opportunities: | Scholarships in Brazil - Master |