Machine Learning Approaches for Precision Risk Assessment of Transfusion Adverse Reactions: A Scikit-learn-Based Review

Summary

This review examines the potential of the scikit-learn machine learning library for predicting transfusion adverse reactions. It outlines a data-driven framework to improve risk assessment, address limitations of conventional approaches, and support the development of precision transfusion medicine.

Abstract

Transfusion adverse reactions (TARs) encompass undesirable effects occurring during or after blood transfusion, resulting from the administration of blood, blood products, or transfusion-associated materials. These reactions may cause mild to severe clinical manifestations, including allergic reactions, hemolysis, and transfusion-related acute lung injury (TRALI). Current conventional approaches -- such as ABO/Rh blood group matching and massive transfusion protocols -- can improve patient outcomes but fail to address complex patient-specific factors. Consequently, developing a dedicated TAR risk model is essential. Machine learning (ML) algorithms can leverage large-scale clinical and laboratory datasets to construct diagnostic and prognostic models, advancing personalized and precision medicine. Notably, the scikit-learn (SK-learn) ML algorithmhas not yet been directly applied to TAR risk assessment; nevertheless, its efficacy in constructing similar medical risk prediction models has gained significant recognition. This review presents an ML-based framework for predicting and preventing TAR, with a specific emphasis on the role of SK-learn across diverse clinical contexts. Our analysis lays the groundwork for future precision diagnosis of TAR and the development of SK-learn algorithms. Crucially, our synthesis of the literature indicates that sklearn offers a versatile and powerful toolkit for this task; however, its successful translation into clinical practice is contingent upon overcoming key challenges, including multi-center data heterogeneity and the need for robust model interpretability.

Introduction

Blood transfusion is a life-saving intervention used in various clinical contexts, including trauma care, surgical procedures, obstetric emergencies, oncological treatments, and the management of chronic hematological disorders^1,2. Despite significant therapeutic benefits, transfusions may cause various adverse events, collectively termed Transfusion Adverse Reactions (TARs)^3,4. TAR represents a multifactorial condition with manifestations ranging from mild allergic reactions to severe complications. The most frequent reactions include febrile non-hemo....