Additional file 2 of 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?

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Publication Details

Journal
Figshare
Published
2025-01-01
DOI
https://doi.org/10.6084/m9.figshare.29926627.v1
Primary Topic
Geochemistry and Geologic Mapping
Type
article
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article

Additional file 2 of 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?

Figshare
Geochemistry and Geologic Mapping
article

Additional file 2 of 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?

article en

Abstract

No abstract available for this paper.
Figshare
University of Illinois Chicago (US), Kaohsiung Chang Gung Memorial Hospital (TW), China Medical University (CN)
Openalex Percentile: Top 72%
Geochemistry and Geologic Mapping
DOISourceOpenAlex
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Additional file 2 of 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? · Figshare (2025) | TGRS Research Map | TGRS