Optimal machine learning methods for prediction of high-flow nasal cannula outcomes using image features from electrical impedance tomography.

Journal: Computer methods and programs in biomedicine
PMID: 37209577

Abstract

BACKGROUND: High-flow nasal cannula (HNFC) is able to provide ventilation support for patients with hypoxic respiratory failure. Early prediction of HFNC outcome is warranted, since failure of HFNC might delay intubation and increase mortality rate. Existing methods require a relatively long period to identify the failure (approximately 12 h) and electrical impedance tomography (EIT) may help identify the patient's respiratory drive during HFNC.

Authors

  • Lin Yang
    National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, China.
  • Zhe Li
  • Meng Dai
    Department of Electronic Engineering, Fudan University, Shanghai, 200433, China.
  • Feng Fu
    Department of Mathematics, Dartmouth College, Hanover, NH 03755, USA.
  • Knut Möller
    Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany.
  • Yuan Gao
    Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou Zhejiang Province, China.
  • Zhanqi Zhao
    Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany.

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