Koch, Gilbert and Pfister, Marc and Daunhawer, Imant and Wilbaux, Melanie and Wellmann, Sven and Vogt, Julia E. (2020) Pharmacometrics and Machine Learning Partner to Advance Clinical Data Analysis. CLINICAL PHARMACOLOGY & THERAPEUTICS, 107 (4). pp. 926-933. ISSN 0009-9236, 1532-6535
Full text not available from this repository. (Request a copy)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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | PHARMACOKINETIC PARAMETERS; MODELS; PHARMACOLOGY; |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Kinder- und Jugendmedizin |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 30 Mar 2021 10:10 |
| Last Modified: | 30 Mar 2021 10:10 |
| URI: | https://pred.uni-regensburg.de/id/eprint/45105 |
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