Accessibility settings

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

Advanced Data Analytics in eHealth (333) Clinical Informatics (2124) Tools, Programs and Algorithms (562) Visualization in eHealth (98) Machine Learning (3029) Digital Biomarkers and Digital Phenotyping (411) Artificial Intelligence (4518)

Published on in Vol 14 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/81145, first published .
Doctors reviewing patient data on tablet and clipboard

Graph Network Feature Space Fusion for Predicting Irregularly Sampled Medical Time-Series Data: Deep Learning Model Development and Validation Study

Graph Network Feature Space Fusion for Predicting Irregularly Sampled Medical Time-Series Data: Deep Learning Model Development and Validation Study

Authors of this article:

Tianle Hong1, 2 Author Orcid Image ;   Zedong Ren2 Author Orcid Image ;   Junfei Fang2 Author Orcid Image ;   Shichao Quan3, 4, 5 Author Orcid Image ;   Jingye Pan2 Author Orcid Image ;   Yezhi Lin2, 5 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Tianle Hong   1, 2 , MEng ;   Zedong Ren   2 , MEng ;   Junfei Fang   2 , MEng ;   Shichao Quan   3, 4, 5 , MM ;   Jingye Pan   2 , PhD ;   Yezhi Lin   2, 5 , PhD

1 Zheyi EENT Hospital of Zhejiang & Eye Ear Nose and Throat Hospital Zhejiang University School of Medicine, Hangzhou, China

2 The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China

3 Department of Big Data in Health Science, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China

4 Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization, Wenzhou, China

5 Zhejiang Key Laboratory of Critical Care Medicine, Wenzhou, China

Corresponding Author:

  • Yezhi Lin, PhD
  • The First Affiliated Hospital of Wenzhou Medical University
  • No. 1, Nanbaixiang Street, Nanbaixiang Sub-district, Ouhai District
  • Wenzhou, Zhejiang 325015
  • China
  • Phone: 86 577 86699816
  • Email: linyezhi@wmu.edu.cn