Application of machine learning in personalized medicine

Abstract

With the deepening of machine learning research in the medical field. At present, more and more studies have applied it to individualized medicine such as drug concentration monitoring and adverse reaction prediction. Compared with traditional population pharmacokinetic modeling methods, machine learning can analyze a large number of real-world medication data. Through multi-level mining of the data, machine learning can more accurately predict blood drug concentration and drug dose, so as to build a more practical individualized medication model, improve the level of clinical precision medication, and reduce the occurrence of adverse reactions. This article reviews the research of machine learning in individualized medicine, in order to provide technical support and theoretical basis for clinical precision medicine.