ArticleClinical pharmacokinetics2024
Machine Learning Approach in Dosage Individualization of Isoniazid for Tuberculosis.
Article in Clinical pharmacokinetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Toward adaptive therapeutic timing: integration of mechanistic pharmacology and artificial intelligence in precision dosing.Frontiers in pharmacology · 2026Review
- Prediction of infliximab and anti-drug antibody concentrations in patients with inflammatory bowel disease using machine learning models with real-world data from a prospective cohort study.Frontiers in pharmacology · 2026Article
- Alpha-1 antitrypsin deficiency: genetics, clinical manifestations, AI prognostics, and advanced imaging in liver disease.Annals of medicine and surgery (2012) · 2025Review
- Methodological Techniques Used in Machine Learning to Support Individualized Drug Dosing Regimens Based on Pharmacokinetic Data: A Scoping Review.Clinical pharmacokinetics · 2025Article
- Global research on the utilization of population pharmacokinetic model: a bibliometric analysis from 2000 to 2024.Frontiers in pharmacology · 2025Review
- Artificial intelligence, medications, pharmacogenomics, and ethics.Pharmacogenomics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
introductionIsoniazid is a first-line antituberculosis agent with high variability, which would profit from individualized dosing. Concentrations of isoniazid at 2 h (C
objectiveThe objective of this study was to establish machine learning (ML) models to predict the C
methodsPublished population pharmacokinetic (PopPK) models for adults were searched based on PubMed and ultimately four reliable models were selected for simulating individual C
resultsCategorical boosting (CatBoost) exhibited the highest prediction ability. Target C
conclusionMachine learning models were developed with great predictive performance, which can be used to determine the individualized initial dose of isoniazid in adult patients.
Indexed as
Identifiers
38990504What Socratic holds
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 Socratic graph.