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ORIGINAL RESEARCH article

Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 7 - 2024 | doi: 10.3389/frai.2024.1343447

A decision support system to recommend appropriate therapy protocol for AML patients

Giovanna A. Castro1* Jade M. Almeida1 João A. Machado-Neto2 Tiago A. Almeida1
  • 1Federal University of Sao Carlos, Brazil
  • 2University of São Paulo, Brazil

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Acute Myeloid Leukemia (AML) is one of the most aggressive hematological neoplasms. Early detection and treatment have been associated with improved patient survival rates, making therapy decisions crucial. To determine the treatment protocol, specialists often rely on prognostic predictions, considering the response to treatment and clinical outcomes. The current risk classification categorizes patients into three groups: favorable, intermediate, and adverse, guiding personalized therapeutic choices. However, the intermediate-risk group is particularly challenging to assess accurately, leading to potential delays in treatment initiation and worsening of patients’ conditions. This study introduces a decision support system leveraging cutting-edge machine learning techniques to address these issues. The system automatically recommends suitable oncology therapy protocols based on outcome predictions. By adopting this approach, we can significantly expedite treatment decisions, leading to prolonged survival and improved quality of life for AML patients.

Keywords: Acute Myeloid Leukemia, Risk classification, Prognostic prediction, Supervised learning model, machine learning, decision support system

Received: 23 Nov 2023; Accepted: 19 Feb 2024.

Copyright: © 2024 Castro, Almeida, Machado-Neto and Almeida. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Giovanna A. Castro, Federal University of Sao Carlos, Sorocaba, 18052-780, Brazil