Machine Learning Techniques Outperform Conventional Statistical Methods in the Prediction of High Risk QTc Prolongation Related to a Drug-Drug Interaction-Reference-Cited by-同舟云学术

Machine Learning Techniques Outperform Conventional Statistical Methods in the Prediction of High Risk QTc Prolongation Related to a Drug-Drug Interaction

Published:2022-11-23 Issue:12 Volume:46 Page:
ISSN:1573-689X
Container-title:Journal of Medical Systems
language:en
Short-container-title:J Med Syst

Author:

Van Laere Sven^ORCID,Muylle Katoo M.^ORCID,Dupont Alain G.^ORCID,Cornu Pieter^ORCID

Funder

Fonds Wetenschappelijk Onderzoek

Publisher

Springer Science and Business Media LLC

Subject

Health Information Management,Health Informatics,Information Systems,Medicine (miscellaneous)

Link

https://link.springer.com/content/pdf/10.1007/s10916-022-01890-4.pdf

Reference41 articles.

1. Khatib, R., Sabir, F. R. N., Omari, C., Pepper, C., and Tayebjee, M. H.,. Managing drug-induced QT prolongation in clinical practice. Postgrad. Med. J. 97(1149):452–458, 2021. https://doi.org/10.1136/postgradmedj-2020-138661.

2. Vorchheimer, D. A., What is QT interval prolongation? J. Fam. Pract. Suppl:S4–S7, 2005.

3. Yap, Y. G., and Camm, A. J., Drug induced QT prolongation and torsades de pointes. Heart. 89(11):1363–72, 2003. https://doi.org/10.1136/heart.89.11.1363.

4. Vandenberk, B., Vandael, E., Robyns, T., Vandenberghe, J., Garweg, C., Foulon, V., Ector, J., et al., Which QT correction formulae to use for QT monitoring? J. Am. Heart Assoc. 5(6):e003264, 2016. https://doi.org/10.1161/jaha.116.003264.

5. Goldenberg, I., Moss, A. J., and Zareba, W., QT interval: how to measure it and what is "normal". J. Cardiovasc. Electrophysiol. 17(3):333–6, 2006. https://doi.org/10.1111/j.1540-8167.2006.00408.x.

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