Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
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.
Recommended Citation
Lino Ferreira Da Silva Barros, Maicon Herverton; Da Silva Neto, Sebastião Rogerio; Almeida Rodrigues, Maria Gabriela; De Souza Sampaio, Vanderson; and Endo, Patricia Takako, "How Artificial Intelligence Can Help the Prediction of Treatment Outcomes of Tuberculosis: A Systematic Literature Review" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 6.
https://aisel.aisnet.org/hicss-56/da/service_analytics/6
How Artificial Intelligence Can Help the Prediction of Treatment Outcomes of Tuberculosis: A Systematic Literature Review
Online
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.
https://aisel.aisnet.org/hicss-56/da/service_analytics/6