Pharmacometrics and Machine Learning Partner to Advance Clinical Data Analysis. Issue 4 (17th February 2020)

• Record Type: Journal Article
• Authors: Koch, Gilbert; Pfister, Marc; Daunhawer, Imant; Wilbaux, Melanie; Wellmann, Sven; Vogt, Julia E.
• Is Part Of: Clinical pharmacology & therapeutics. Volume 107:Issue 4(2020)
• Page Start: 926
• Page End: 933
• Publication Date: 2020-02-17
• Subjects: Pharmacology -- Periodicals; Therapeutics -- Periodicals; 615.5
• DOI: 10.1002/cpt.1774
• Languages: English
• ISSNs: 0009-9236

Abstract

Abstract : 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.