Abstract

Although allogeneic hematopoietic stem cell transplantation (allo‐HSCT) is a curative therapy for high‐risk acute leukemia (AL), some patients still relapse. Since patients simultaneously have many prognostic factors, difficulties are associated with the construction of a patient‐based prediction algorithm of relapse. The alternating decision tree (ADTree) is a successful classification method that combines decision trees with the predictive accuracy of boosting. It is a component of machine learning (ML) and has the capacity to simultaneously analyze multiple factors. Using ADTree, we attempted to construct a prediction model of leukemia relapse within 1 year of transplantation. With the model of training data (n = 148), prediction accuracy, the AUC of ROC, and the κ‐statistic value were 78.4%, 0.746, and 0.508, respectively. The false positive rate (FPR) of the relapse prediction was as low as 0.134. In an evaluation of the model with validation data (n = 69), prediction accuracy, AUC, and FPR of the relapse prediction were similar at 71.0%, 0.667, and 0.216, respectively. These results suggest that the model is generalized and highly accurate. Furthermore, the output of ADTree may visualize the branch point of treatment. For example, the selection of donor types resulted in different relapse predictions. Therefore, clinicians may change treatment options by referring to the model, thereby improving outcomes. The present results indicate that ML, such as ADTree, will contribute to the decision‐making process in the diversified allo‐HSCT field and be useful for preventing the relapse of leukemia.

Highlights

  • The complex network of multiple factors in a patient makes the patient‐based prediction of relapse, which is generally useful in the bedside decision‐making process regarding an indication for or the protocol of allo‐HSCT, difficult

  • Since machine learning (ML) has the capacity to analyze multiple factors, we attempted to generate robust and accurate prediction models of relapse after allo‐HSCT, which may be a useful tool in the bedside decision‐making process to select a transplant method for reducing the relapse of leukemia

  • artificial intelligence (AI) and ML were initially developed for image and voice recognition and were subsequently applied to the analysis of data sets of large volumes, such as purchase records.[18]

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Summary

| INTRODUCTION

Allogeneic hematopoietic stem cell transplantation (allo‐ HSCT) is an established therapy that is associated with a high rate of curability for acute leukemia (AL).[1,2,3] many patients still relapse after allo‐HSCT, with common causes of death being relapse and leukemia‐associated complications.[3,4] Since salvage therapy is limited for these patients, their prognosis is very poor, with a probability of long‐term survival of

| METHODS
| DISCUSSION
Findings
CONFLICT OF INTEREST
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