Emerging Concepts and Applied Machine Learning Research in Patients with Drug-Induced Repolarization Disorders

Details

Serval ID
serval:BIB_09F22A044FFC
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Emerging Concepts and Applied Machine Learning Research in Patients with Drug-Induced Repolarization Disorders
Journal
Stud Health Technol Inform
Author(s)
Bjelogrlic M., Robert A., Miribel A., Namdar M., Gencer B., Lovis C., Girardin F.
ISSN
1879-8365 (Electronic)
ISSN-L
0926-9630
Publication state
Published
Issued date
2020
Volume
270
Pages
198-202
Language
english
Notes
Bjelogrlic, Mina
Robert, Arnaud
Miribel, Arnaud
Namdar, Mehdi
Gencer, Baris
Lovis, Christian
Girardin, Francois
eng
Review
Netherlands
Stud Health Technol Inform. 2020 Jun 16;270:198-202. doi: 10.3233/SHTI200150.
Abstract
The paper presents a review of current research to develop predictive models for automated detection of drug-induced repolarization disorders and shows a feasibility study for developing machine learning tools trained on massive multimodal datasets of narrative, textual and electrocardiographic records. The goal is to reduce drug-induced long QT and associated complications (Torsades-de-Pointes, sudden cardiac death), by identifying prescription patterns with pro-arrhythmic propensity using a validated electronic application for the detection of adverse drug events with data mining and natural language processing; and to compute individual-based predictive scores in order to further identify clinical conditions, concomitant diseases, or other variables that correlate with higher risk of pro-arrhythmic situations.
Keywords
Death, Sudden, Cardiac, Electrocardiography, Humans, Long QT Syndrome, *Machine Learning, Torsades de Pointes, Adverse Drug Events, Analytic-Decision Modelling, Clinical Decision Support System, Long QT, Machine Learning, Pharmacovigilance, Repolarization Disorders, Torsades-de-Pointes
Pubmed
Create date
10/02/2021 12:32
Last modification date
11/02/2021 7:26
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