A decision support system to recommend appropriate therapy protocol for AML patients - Castro, Giovanna A.; Almeida, Jade M.; Machado-Neto, João A.; Almeida, Tiago A. (Frontiers in Artificial Intelligence, 2024)
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.