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

doi:10.1111/jth.15318

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

Community

Back to search

Article

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

Journal of thrombosis and haemostasis : JTH - 1 Jul 2021

Nguyen Van Lam, Nguyen Hoang Dat, Cho Yong-Soon, Kim Ho-Sook, Han Il-Yong, Kim Dae-Kyeong, Ahn Sangzin, Shin Jae-Gook

Abstract excerpt

BACKGROUND: Personalized warfarin dosing is influenced by various factors including genetic and non-genetic factors. Multiple linear regression (LR) is known as a conventional method to develop predictive models. Recently, machine learning approaches have been extensively implemented for warfarin dosing due to the hypothesis of non-linear association between covariates and stable warfarin dose. OBJECTIVE: To...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Anticoagulants
Share to Cytochrome P-450 CYP2C9

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Linear Models

Share to Machine Learning

Share to Republic of Korea

Share to Vitamin K Epoxide Reductases

Share to Warfarin

Source

Open in PubMed

Identifiers

PMID: 33774911
DOI: 10.1111/jth.15318

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.