An Introduction to Machine Learning

Abstract

In the last few years, machine learning (ML) and artificial intelligence have seen a new wave of publicity fueled by the huge and ever‐increasing amount of data and computational power as well as the discovery of improved learning algorithms. However, the idea of a computer learning some abstract concept from data and applying them to yet unseen situations is not new and has been around at least since the 1950s. Many of these basic principles are very familiar to the pharmacometrics and clinical pharmacology community. In this paper, we want to introduce the foundational ideas of ML to this community such that readers obtain the essential tools they need to understand publications on the topic. Although we will not go into the very details and theoretical background, we aim to point readers to relevant literature and put applications of ML in molecular biology as well as the fields of pharmacometrics and clinical pharmacology into perspective.

From Real‐World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges

“…The interplay between machine learning and pharmacometric and QSP approaches represents an important research frontier, which has also been explored by Ribba et al 16 in the context of integrating PK/PD modeling into reinforcement learning algorithms for precision dosing. In addition, Koch et al 82 illustrate the benefits of incorporating pharmacometrics into machine learning for classification of clinically relevant patient outcomes.…”

“…The interplay between machine learning and pharmacometric and QSP approaches represents an important research frontier, which has also been explored by Ribba et al 16 . in the context of integrating PK/PD modeling into reinforcement learning algorithms for precision dosing.…”

“…In this tutorial, we survey the recent progress that has been made in using observational data for building data‐driven methods for making treatment recommendations. We refer readers to the recent tutorial on machine learning methods for a background of the general approaches 16 or to elsewhere as appropriate. Our focus is on a causal inference method for both the static (cross‐sectional) setting and the longitudinal setting (where patient history and treatment timing are taken into account).…”

Data Science in Clinical Pharmacology and Drug Development for Improving Health Outcomes in Patients

“…The second tutorial in this issue provides a primer in basic machine learning to demystify some concepts, methods, and applications. 23 In our view, there are many exciting opportunities for machine learning to help clinical pharmacologists and drug discovery and development. 24,25 Often the biggest advances occur when different disciplines intersect, and pharmacometrics should be fertile ground to benefit from machine learning-indeed there is already quite a literature on this topic.…”

Clinical Pharmacology and Therapeutics: 2020 in Review

“…2 This was also the first example of an issue fully dedicated to a particular theme, including all original research articles. It also introduced the first CPT Tutorials, 3,4 a new article type for the journal providing practical educational material on tools, methodologies, and approaches in clinical pharmacology [5][6][7] ( Table 1).…”