Clinical pharmacology is a multidisciplinary data sciences field that utilizes mathematical and statistical methods to generate maximal knowledge from data. Pharmacometrics (PMX) is a well-recognized tool to characterize disease progression, pharmacokinetics, and risk factors. Because the amount of data produced keeps growing with increasing pace, the computational effort necessary for PMX models is also increasing. Additionally, computationally efficient methods, such as machine learning (ML) are becoming increasingly important in medicine. However, ML is currently not an integrated part of PMX, for various reasons. The goals of this article are to (i) provide an introduction to ML classification methods, (ii) provide examples for a ML classification analysis to identify covariates based on specific research questions, (iii) examine a clinically relevant example to investigate possible relationships of ML and PMX, and (iv) present a summary of ML and PMX tasks to develop clinical decision support tools.

中文翻译：

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药理学和机器学习合作伙伴，以推进临床数据分析。

临床药理学是一个多学科的数据科学领域，它利用数学和统计方法从数据中获得最大的知识。药效学（PMX）是公认的用于表征疾病进展，药代动力学和危险因素的工具。由于生成的数据量不断增长，因此PMX模型所需的计算量也在增加。另外，诸如机器学习（ML）之类的计算有效方法在医学中变得越来越重要。但是，由于各种原因，ML当前不是PMX的集成部分。本文的目的是（i）介绍ML分类方法，（ii）提供ML分类分析的示例，以根据特定的研究问题识别协变量，