This paper is in the following e-collection/theme issue:

Decision Support for Health Professionals (1448) Data Science (342) Nephrology/Renal Disorders (90) Cardiac Diagnostics (26) Cardiac Arrhythmia, Atrial Fibrillation (112) Machine Learning (1933)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/73353, first published .
Cardiorenal Interorgan Assessment via a Novel Clustering Method Using Dynamic Time Warping on Electrocardiogram: Model Development and Validation Study

Cardiorenal Interorgan Assessment via a Novel Clustering Method Using Dynamic Time Warping on Electrocardiogram: Model Development and Validation Study

Cardiorenal Interorgan Assessment via a Novel Clustering Method Using Dynamic Time Warping on Electrocardiogram: Model Development and Validation Study

Authors of this article:

Sally Zhao1 Author Orcid Image ;   Zhan Ye2 Author Orcid Image ;   Bhavna Adhin3 Author Orcid Image ;   Matti Vuori4, 5 Author Orcid Image ;   Jari Laukkanen6, 7 Author Orcid Image ;   FinnGen 8 ;   Sudeshna Fisch2 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Sally Zhao   1 , MS ;   Zhan Ye   2 , PhD ;   Bhavna Adhin   3 , MS ;   Matti Vuori   4, 5 , LM ;   Jari Laukkanen   6, 7 , PhD ;   FinnGen   8 ;   Sudeshna Fisch   2 , PhD

1 Pfizer (United States), New York, United States

2 Pfizer (United States), Boston, MA, United States

3 Pfizer (United States), Austin, TX, United States

4 Turku University Hospital, Turku, Finland

5 Institute for Molecular Medicine Finland, Helsinki, Finland

6 Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland

7 Department of Medicine, Wellbeing Services County of Central Finland, Jyväskylä, Finland

8 University of Helsinki, Helsinki, Finland

Corresponding Author: