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

Machine Learning (1971) Artificial Intelligence (1996) Big Data (305) Novel Sensors and Data Acquisition Methods in Cardiology (223) Advanced Data Analytics in eHealth (196)

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 (13) 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(7) View
  9. Aslanidou L, Rovas G, Mohammadi R, Anagnostopoulos S, Çelikbudak Orhon C, Stergiopulos N. Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population. Scientific Reports 2025;15(1) View
  10. Lee A, Lee J, Yoo S, Lee H, Kim H. Real-Time Estimation of Arterial Partial Pressure of Carbon Dioxide in Patients Undergoing General Anesthesia: Predictive Modeling Study. JMIR Medical Informatics 2025;13:e64855 View

Books/Policy Documents

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

Conference Proceedings

  1. Van Mierlo R, Bouwman R, Van Riel N. 2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA). Reproducing and Improving One-Dimensional Convolutional Neural Networks for Arterial Blood Pressure-Based Cardiac Output Estimation View
  2. Hamo A, Ottenhof N, Korstanje J, Dauwels J. 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Machine Learning Algorithm to Estimate Cardiac Output Based On Less-Invasive Arterial Blood Pressure Measurements View