How Artificial Intelligence Can Help the Prediction of Treatment Outcomes of Tuberculosis: A Systematic Literature Review

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0136.pdf (303.86 KB)
Date
2023-01-03
Authors
Lino Ferreira Da Silva Barros, Maicon Herverton
Da Silva Neto, Sebastião Rogerio
Almeida Rodrigues, Maria Gabriela
De Souza Sampaio, Vanderson
Endo, Patricia Takako
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1386
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Abstract
Tuberculosis (TB) is a disease with a global impact that over the years has mainly affected the poorest countries. After confirming the TB diagnosis, the health professional needs to analyze the severity of the clinical situation of the patient in order to make decisions about their treatment, which may include admission to Intensive Care Unit (ICU). The aim of this paper is to present a systematic review focused on Machine Learning (ML) models for predicting TB treatment outcomes. From 253 articles found through a boolean search, only 12 of them were classified as relevant, presented and discussed in this work. Results show that the current literature is focused on binary classification, mainly using tree-based ML algorithms. Based on the results of this systematic review, we state that there are many opportunities to develop new scientific projects in this area, highlighting the need for rigorous methodology to conduct models' configuration as well as experiments to evaluate them.
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Service Analytics, artificial intelligence, prediction, prognosis, treatment outcomes, tuberculosis
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https://hdl.handle.net/10125/102803
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10
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Proceedings of the 56th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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Service Analytics
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