ArticleClinical pharmacology and therapeutics2020
Pharmacometrics and Machine Learning Partner to Advance Clinical Data Analysis.
Article in Clinical pharmacology and therapeutics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed.
- Contribution of machine learning to tumor growth inhibition modeling for hepatocellular carcinoma patients under Roblitinib (FGF401) drug treatment.CPT: pharmacometrics & systems pharmacology · 2022Trial
- Pharmacometric modeling and machine learning analyses of prognostic and predictive factors in the JAVELIN Gastric 100 phase III trial of avelumab.CPT: pharmacometrics & systems pharmacology · 2022Trial
- Early predictions of response and survival from a tumor dynamics model in patients with recurrent, metastatic head and neck squamous cell carcinoma treated with immunotherapy.CPT: pharmacometrics & systems pharmacology · 2021Trial
- The evolution of nonlinear mixed effects modeling in pharmacometrics: toward AI-based variational autoencoders.Journal of pharmacokinetics and pharmacodynamics · 2026Article
- Development and validation of a machine learning model for individualised meropenem dosing in CRRT patients.Scientific reports · 2026Article
- A Pharmacometrics-Informed Trial Simulation Framework for Optimizing Study Designs for Disease-Modifying Treatments in Rare Neurological Disorders.CPT: pharmacometrics & systems pharmacology · 2025Article
- Effect of Cumulative Exposure on the Efficacy of Paroxetine: A Population Pharmacokinetic-Pharmacodynamic and Machine Learning Analyses.CPT: pharmacometrics & systems pharmacology · 2025Article
- Artificial Intelligence and Machine Learning Applications to Pharmacokinetic Modeling and Dose Prediction of Antibiotics: A Scoping Review.Antibiotics (Basel, Switzerland) · 2024Article
- pyDarwin machine learning algorithms application and comparison in nonlinear mixed-effect model selection and optimization.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Go beyond the limits of genetic algorithm in daily covariate selection practice.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Low-dimensional neural ODEs and their application in pharmacokinetics.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Application of machine learning combined with population pharmacokinetics to improve individual prediction of vancomycin clearance in simulated adult patients.Frontiers in pharmacology · 2024Article
- Modeling tumor size dynamics based on real-world electronic health records and image data in advanced melanoma patients receiving immunotherapy.CPT: pharmacometrics & systems pharmacology · 2023Article
- Deep Learning Methods Applied to Drug Concentration Prediction of Olanzapine.Pharmaceutics · 2023Article
- Computational design of clinical trials using a combination of simulation and the genetic algorithm.CPT: pharmacometrics & systems pharmacology · 2023Article
- A Hybrid Algorithm Combining Population Pharmacokinetic and Machine Learning for Isavuconazole Exposure Prediction.Pharmaceutical research · 2023Article
- Revolutionizing antimicrobial stewardship, infection prevention, and public health with artificial intelligence: the middle path.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2023Article
- Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: an exploratory analysis.Frontiers in clinical diabetes and healthcare · 2023Article
- A Systems-Based Analysis of Mono- and Combination Therapy for Carbapenem-Resistant Klebsiella pneumoniae Bloodstream Infections.Antimicrobial agents and chemotherapy · 2022Article
- Machine Learning and Pharmacometrics for Prediction of Pharmacokinetic Data: Differences, Similarities and Challenges Illustrated with Rifampicin.Pharmaceutics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.