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

Machine Learning (1613) Artificial Intelligence (1581) Big Data (265) Novel Sensors and Data Acquisition Methods in Cardiology (208) Advanced Data Analytics in eHealth (150)

Published on in Vol 9, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24762, first published .
Development and Validation of an Arterial Pressure-Based Cardiac Output Algorithm Using a Convolutional Neural Network: Retrospective Study Based on Prospective Registry Data

Development and Validation of an Arterial Pressure-Based Cardiac Output Algorithm Using a Convolutional Neural Network: Retrospective Study Based on Prospective Registry Data

Development and Validation of an Arterial Pressure-Based Cardiac Output Algorithm Using a Convolutional Neural Network: Retrospective Study Based on Prospective Registry Data

Authors of this article:

Hyun-Lim Yang1, 2 Author Orcid Image ;   Chul-Woo Jung1, 3 Author Orcid Image ;   Seong Mi Yang1, 3 Author Orcid Image ;   Min-Soo Kim4 Author Orcid Image ;   Sungho Shim5 Author Orcid Image ;   Kook Hyun Lee1, 3 Author Orcid Image ;   Hyung-Chul Lee1, 3 Author Orcid Image
Article Authors Cited by (11) Tweetations (3) Metrics

Journals

  1. Lee H, Park Y, Yoon S, Yang S, Park D, Jung C. VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients. Scientific Data 2022;9(1) View
  2. Hsiao W, Kan Y, Kuo C, Kuo Y, Chai S, Lin H. Hybrid-Pattern Recognition Modeling with Arrhythmia Signal Processing for Ubiquitous Health Management. Sensors 2022;22(2):689 View
  3. Dervishi A. A multimodal stacked ensemble model for cardiac output prediction utilizing cardiorespiratory interactions during general anesthesia. Scientific Reports 2024;14(1) View
  4. Yuan S, Zhou Y, Chen J, Zhang X, Wang G. Anesthesia for lung transplantation in children under 12 years of age: a single center experience of China. Anesthesiology and Perioperative Science 2024;2(2) View
  5. Iscan M, Yesildirek A. An intelligent aortic valve model for complete cardiac cycle. International Journal for Numerical Methods in Biomedical Engineering 2024;40(8) View
  6. Yang H, Park S, Lee H, Lee H, Ryu H. Feasibility of estimating tidal volume from electrocardiograph-derived respiration signal and respiration waveform. Journal of Critical Care 2025;85:154920 View
  7. Liu J, Zhu H, Xiang W, Hu S, Hu Q, Wang D, Yang H, Mao Z, Xu F, Yang C. An IoMT-Driven Framework for Precision Cardiovascular Assessment Incorporating Multiscale Perspectives and Microfiber Bragg Grating. IEEE Internet of Things Journal 2025;12(4):4050 View
  8. Liao K, Elibol A, Gao Z, Meng L, Chong N. Predicting hemodynamic parameters based on arterial blood pressure waveform using self-supervised learning and fine-tuning. Applied Intelligence 2025;55(6) View

Books/Policy Documents

  1. Park S, Lee H, Jung C, Yang H. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. View