Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Evaluation of supervised machine learning algorithms in predicting the poor anticoagulation control and stable weekly doses of warfarin.

International journal of clinical pharmacy - 1 Feb 2023

Sridharan Kannan, Ramanathan Murali, Al Banna Rashed

Abstract excerpt

BACKGROUND: Machine learning algorithms (MLAs) carry a huge potential in identifying predicting factors and are being explored for their utility in the field of personalized medicine. AIM: We aimed to investigate MLAs for identifying predictors (clinical and genetic) of poor anticoagulation status (ACS) and stable weekly warfarin dose (SWWD). METHOD: Clinical factors, in addition to the CYP2C9, VKORC1, and CYP4F2...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Anticoagulants
Share to Aryl Hydrocarbon Hydroxylases

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Cytochrome P-450 CYP2C9

Share to Cytochrome P450 Family 4

Share to Pharmacogenetics

Share to Supervised Machine Learning

Share to Vitamin K Epoxide Reductases

Share to Warfarin

Source

Identifiers

PMID: 36306062
DOI: 10.1007/s11096-022-01471-y

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Evaluation of supervised machine learning algorithms in predicting the poor anticoagulation control and stable weekly doses of warfarin.PMID 36306062Read ↗

Comparison of multivariate linear regression and a machine learning algorithm developed for prediction of precision warfarin dosing in a Korean population.Semantic proximity Read ↗

Warfarin dosing algorithms: A systematic review.Semantic proximity Read ↗

Predictors of warfarin therapeutic dose across thromboembolic indications: an exploratory early clinical evaluation study using machine learning-integrated explainable artificial intelligence and Bayesian models.Semantic proximity Read ↗

A new warfarin dosing algorithm including VKORC1 3730 G > A polymorphism: comparison with results obtained by other published algorithms.Semantic proximity Read ↗

Validation of warfarin pharmacogenetic algorithms in 586 Han Chinese patients.Semantic proximity Read ↗

Creating a genotype-based dosing algorithm for acenocoumarol steady dose.Semantic proximity Read ↗

Validation of warfarin pharmacogenetic algorithms in clinical practice.Semantic proximity Read ↗

Comparison of the Performance of the Warfarin Pharmacogenetics Algorithms in Patients with Surgery of Heart Valve Replacement and Heart Valvuloplasty.Semantic proximity Read ↗

Computational Structural Validation of CYP2C9 Mutations and Evaluation of Machine Learning Algorithms in Predicting the Therapeutic Outcomes of Warfarin.Semantic proximity Read ↗

High-dimensional pharmacogenetic prediction of a continuous trait using machine learning techniques with application to warfarin dose prediction in African Americans.Semantic proximity Read ↗

Automated warfarin dose prediction for Asian, American, and Caucasian populations using a deep neural network.Semantic proximity Read ↗

Effect of CYP2C9-VKORC1 interaction on warfarin stable dosage and its predictive algorithm.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.