Risk prediction of QTc prolongation occurrence in cancer patients treated with commonly used oral tyrosine kinase inhibitors: machine learning modeling or conventional statistical analysis better?

Risk prediction of QTc prolongation occurrence in cancer patients treated with commonly used oral tyrosine kinase inhibitors: machine learning modeling or conventional statistical analysis better?

Abstract Background Cancer patients receiving targeted therapies need to prevent QTc prolongation and life-threatening cardiovascular (CV) events to maintain a balanced benefit-risk ratio. This study aimed to develop an optimal prediction model for QTc prolongation risk and estimate its risk probabi...

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Bibliographic Details
Main Authors: Hsiang-Wen Lin, Tien-Chao Lin, Chien-Ning Hsu, Tzu-Pei Yeh, Yu-Chieh Chen, Liang-Chih Liu, Chen-Yuan Lin
Format: Article
Language:English
Published: BMC 2025-08-01
Series:BMC Medical Informatics and Decision Making
Subjects:
QTc prolongation
Oral
Tyrosine kinase inhibitors
Logistic regression
Machine learning
Algorithm
Online Access:https://doi.org/10.1186/s12911-025-03091-8
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https://doi.org/10.1186/s12911-025-03091-8

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