An explainable model to support the decision about the therapy protocol for AML

TL;DR

This paper introduces an explainable machine-learning model that predicts survival outcomes for AML patients, aiming to improve therapy decision-making and address limitations of current risk classifications.

Contribution

The study develops a novel, interpretable ML model for AML prognosis that enhances decision support and offers insights for future research on treatments and prognostic markers.

Findings

Model provides accurate survival predictions

Supports clinicians in therapy decisions

Potential to improve AML patient outcomes

Abstract

Acute Myeloid Leukemia (AML) is one of the most aggressive types of hematological neoplasm. To support the specialists' decision about the appropriate therapy, patients with AML receive a prognostic of outcomes according to their cytogenetic and molecular characteristics, often divided into three risk categories: favorable, intermediate, and adverse. However, the current risk classification has known problems, such as the heterogeneity between patients of the same risk group and no clear definition of the intermediate risk category. Moreover, as most patients with AML receive an intermediate-risk classification, specialists often demand other tests and analyses, leading to delayed treatment and worsening of the patient's clinical condition. This paper presents the data analysis and an explainable machine-learning model to support the decision about the most appropriate therapy protocol according to the patient's survival prediction. In addition to the prediction model being explainable, the results obtained are promising and indicate that it is possible to use it to support the specialists' decisions safely. Most importantly, the findings offered in this study have the potential to open new avenues of research toward better treatments and prognostic markers.