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

Artificial Intelligence (2129) Machine Learning (2076) Clinical Information and Decision Making (1915) Decision Support for Health Professionals (1539) Research Instruments, Questionnaires, and Tools (738) Tools, Programs and Algorithms (376) Safety and Error Prevention in Health (240)

Published on in Vol 23, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27098, first published .
Machine Learning Analysis of Time-Dependent Features for Predicting Adverse Events During Hemodialysis Therapy: Model Development and Validation Study

Machine Learning Analysis of Time-Dependent Features for Predicting Adverse Events During Hemodialysis Therapy: Model Development and Validation Study

Machine Learning Analysis of Time-Dependent Features for Predicting Adverse Events During Hemodialysis Therapy: Model Development and Validation Study

Authors of this article:

Yi-Shiuan Liu1, 2, 3, 4 Author Orcid Image ;   Chih-Yu Yang1, 2, 5, 6 Author Orcid Image ;   Ping-Fang Chiu7 Author Orcid Image ;   Hui-Chu Lin2 Author Orcid Image ;   Chung-Chuan Lo8 Author Orcid Image ;   Alan Szu-Han Lai2 Author Orcid Image ;   Chia-Chu Chang9, 10 Author Orcid Image ;   Oscar Kuang-Sheng Lee1, 2, 11 Author Orcid Image
Article Authors Cited by (12) Tweetations (4) Metrics

Journals

  1. Elbasha A, Naga Y, Othman M, Moussa N, Elwakil H. A step towards the application of an artificial intelligence model in the prediction of intradialytic complications. Alexandria Journal of Medicine 2022;58(1):18 View
  2. Lu Y, Chao H, Chiang Y, Chen H. Explainable Machine Learning Techniques To Predict Amiodarone-Induced Thyroid Dysfunction Risk: Multicenter, Retrospective Study With External Validation. Journal of Medical Internet Research 2023;25:e43734 View
  3. Othman M, Elbasha A, Naga Y, Moussa N. Early prediction of hemodialysis complications employing ensemble techniques. BioMedical Engineering OnLine 2022;21(1) View
  4. Dong J, wang K, He J, Guo Q, Min H, Tang D, Zhang Z, Zhang C, Zheng F, Li Y, Xu H, Wang G, Luan S, Yin L, Zhang X, Dai Y. Machine learning-based intradialytic hypotension prediction of patients undergoing hemodialysis: A multicenter retrospective study. Computer Methods and Programs in Biomedicine 2023;240:107698 View
  5. Lee W, Fang Y, Chang W, Hsiao K, Shia B, Chen M, Tsai M. Data-driven, two-stage machine learning algorithm-based prediction scheme for assessing 1-year and 3-year mortality risk in chronic hemodialysis patients. Scientific Reports 2023;13(1) View
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  7. Zhu W, Li Z, Su H, Liu L, Heidari A, Chen H, Liang G. Optimizing microseismic monitoring: a fusion of Gaussian–Cauchy and adaptive weight strategies. Journal of Computational Design and Engineering 2024;11(5):1 View
  8. KV J, Bhardwaj U, Gohil J, Biswal J, Nimesh R, Dhingra L, Patil V. Machine learning for Forecasting quality of life variations in hemodialysis patients. Health Leadership and Quality of Life 2024;3 View
  9. Wu R, Ye C, Liu L, Li X, Chen L. Multiverse optimization with information sharing and spiraling communication: Enhancing multithreshold image segmentation for colon cancer. Journal of Computational Design and Engineering 2025;12(5):95 View
  10. Wang J, Yin X, Li Z, Liang P, Wang Y, Li X, Qiao W, Xiong C, Yu M, Ding X, Wang X. Single-Cell RNA Sequencing Integrated with Bulk-RNA Sequencing Analysis Reveals Prognostic Signatures Based on PANoptosis in Hepatocellular Carcinoma. Journal of Hepatocellular Carcinoma 2025;Volume 12:1661 View
  11. Jayousi S, Cinelli M, Bigazzi R, Bianchi S. Functionality and sustainability of telemedicine for home-based management of patients with chronic kidney disease: the telemechron study. Journal of Nephrology 2025;38(8):2239 View
  12. Lin C, Shih H, Chen Y, Chen S, Su H, Chen H, Wu C. The BestShape artificial intelligence system for real-time prediction of intradialytic hypotension—clinical outcomes after four-year follow-up. Clinical Kidney Journal 2025;18(11) View