Construction of a blood drug concentration estimation modelusing a small number of data with biased data distribution

Abstract

Drug administration is performed by various treatments. Drug administration is not easy, and there is a high risk of side effects and sequelae if there are errors in the plans such as dosage and administration inter val. Therefore, it is important to estimate the blood drug concentration in advance. In previous studies, the use of neural networks has made it possible to exceed the prediction accuracy of drug motion analysis proposed in the field of pharmacy. However, the blood drug concentrations recorded in the data used are few and biased.

In this study, in order to estimate the blood drug concentration using a small number of data with a biased data distribution, a method to equalize the data by removing the data in the large part of the data distribution and A method is used to equalize the data by using GAN (Generative Adversarial Network) to generate pseudo data using the data in the area where the data distribution is small and adding the generated data as training data of the original data.