ArticleComputer methods in applied mechanics and engineering2019
Machine learning in drug development: Characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification.
Article in Computer methods in applied mechanics and engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 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
46 citing papers in PubMed, 100 citations in OpenAlex.
- Multiscale model calibrated with a Bayesian algorithm predicts long-term outcomes of treatments for mitral valve regurgitation in animal models.Biomechanics and modeling in mechanobiology · 2026Article
- Uncertainty Quantification with Domain Classification Models for Acetylcholinesterase Inhibition.Journal of chemical information and modeling · 2026Article
- Flowerbed-inspired biomimetic 3D-printed scaffolds functionalized with urine-derived stem cell exosomes promote alveolar bone regeneration by regulating energy metabolism.Theranostics · 2026Article
- Full-field surrogate modeling of cardiac electrophysiology encoding geometric variability.Computer methods in applied mechanics and engineering · 2026Article
- Artificial intelligence in preclinical epilepsy research: Current state, potential, and challenges.Epilepsia open · 2025Review
- Assessing post-TAVR cardiac conduction abnormalities risk using an electromechanically coupled beating heart.Biomechanics and modeling in mechanobiology · 2025Article
- Multifidelity deep learning modeling of spatiotemporal lung mechanics.Frontiers in physiology · 2025Article
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
- Fast and accurate prediction of drug induced proarrhythmic risk with sex specific cardiac emulators.NPJ digital medicine · 2024Article
- Reconstructing blood flow in data-poor regimes: a vasculature network kernel for Gaussian process regression.Journal of the Royal Society, Interface · 2024Article
- Digital twinning of cardiac electrophysiology for congenital heart disease.Journal of the Royal Society, Interface · 2024Article
- Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.Circulation · 2024Review
- Digital twinning of cardiac electrophysiology for congenital heart disease.bioRxiv : the preprint server for biology · 2023Article
- Artificial Intelligence ECG Analysis in Patients with Short QT Syndrome to Predict Life-Threatening Arrhythmic Events.Sensors (Basel, Switzerland) · 2023Article
- 3D-Printed Functional Hydrogel by DNA-Induced Biomineralization for Accelerated Diabetic Wound Healing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Can machine learning accelerate soft material parameter identification from complex mechanical test data?Biomechanics and modeling in mechanobiology · 2023Article
- Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias.Frontiers in physiology · 2022Article
- Machine learning techniques for arrhythmic risk stratification: a review of the literature.International journal of arrhythmia · 2022Article
- Drug Treatment Effect Model Based on MODWT and Hawkes Self-Exciting Point Process.Computational and mathematical methods in medicine · 2022Article
- Dimensionality Reduction in Surrogate Modeling: A Review of Combined Methods.Data science and engineering · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Prolonged QT intervals are a major risk factor for ventricular arrhythmias and a leading cause of sudden cardiac death. Various drugs are known to trigger QT interval prolongation and increase the proarrhythmic potential. Yet, how precisely the action of drugs on the cellular level translates into QT interval prolongation on the whole organ level remains insufficiently understood. Here we use machine learning techniques to systematically characterize the effect of 30 common drugs on the QT interval. We combine information from high fidelity three-dimensional human heart simulations with low fidelity one-dimensional cable simulations to build a surrogate model for the QT interval using multi-fidelity Gaussian process regression. Once trained and cross-validated, we apply our surrogate model to perform sensitivity analysis and uncertainty quantification. Our sensitivity analysis suggests that compounds that block the rapid delayed rectifier potassium current
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.