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

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...

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Bibliographic Details
Main Authors:	Dai, M. (Author), Fu, F. (Author), Gao, Y. (Author), Li, Z. (Author), Möller, K. (Author), Yang, L. (Author), Zhao, Z. (Author)
Format:	Article
Language:	English
Published:	Elsevier Ireland Ltd 2023
Subjects:	Adaptive boosting adult area under the curve article artificial neural network Balancing Bernoulli Boosted decision trees clinical article computer assisted impedance tomography controlled study decision tree Decision trees Digital storage Early prediction Electric impedance Electric impedance measurement Electric impedance tomography Electrical impe dance tomography (EIT) Electrical impedance tomography feature selection Feature Selection female Forecasting High flow high flow nasal cannula therapy High-flow nasal cannula human human tissue Image features k nearest neighbor machine learning Machine learning Machine learning methods Machine-learning male Nearest neighbor search Neural networks outcome assessment Particle swarm optimization (PSO) prediction random forest Random forests recall standardization support vector machine Support vector machines
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Optimal machine learning methods for prediction of high-flow nasal cannula outcomes using image features from electrical impedance tomography

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