Zobrazeno 1 - 5
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pro vyhledávání: '"Hengji Chen"'
Publikováno v:
PLoS ONE, Vol 19, Iss 1, p e0297519 (2024)
Pulmonary function tests (PFTs) are usually interpreted by clinicians using rule-based strategies and pattern recognition. The interpretation, however, has variabilities due to patient and interpreter errors. Most PFTs have recognizable patterns that
Externí odkaz:
https://doaj.org/article/d599f96fe0244fcda367d2ae52a21598
Autor:
Hengji Chen, Sangeeta Joshi, Amber J. Oberle, An-Kwok Wong, David Shaz, Suman Thapamagar, Laren Tan, James D. Anholm, Paresh C. Giri, Craig Henriquez, Yuh-Chin T. Huang
Publikováno v:
Frontiers in Physiology, Vol 13 (2022)
Excessive decrease in the flow of the late expiratory portion of a flow volume loop (FVL) or “flattening”, reflects small airway dysfunction. The assessment of the flattening is currently determined by visual inspection by the pulmonary function
Externí odkaz:
https://doaj.org/article/3813fa5367294690a79df5366dd597f8
Autor:
Paresh C. Giri, Anand M. Chowdhury, Armando Bedoya, Hengji Chen, Hyun Suk Lee, Patty Lee, Craig Henriquez, Neil R. MacIntyre, Yuh-Chin T. Huang
Publikováno v:
Frontiers in Physiology, Vol 12 (2021)
Analysis of pulmonary function tests (PFTs) is an area where machine learning (ML) may benefit clinicians, researchers, and the patients. PFT measures spirometry, lung volumes, and carbon monoxide diffusion capacity of the lung (DLCO). The results ar
Externí odkaz:
https://doaj.org/article/bf9c62bc61b0455ead503e0ee2e4f8ce
Publikováno v:
2023 11th International IEEE/EMBS Conference on Neural Engineering (NER).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d8631a757ca99a223bce3e49c28eba67
https://doi.org/10.1109/ner52421.2023.10123731
https://doi.org/10.1109/ner52421.2023.10123731
Autor:
Yuh-Chin T. Huang, Neil R. MacIntyre, Craig S. Henriquez, Anand M. Chowdhury, Armando Bedoya, Paresh C. Giri, Hengji Chen, Hyun Suk Lee, Patty J. Lee
Publikováno v:
Frontiers in Physiology
Frontiers in Physiology, Vol 12 (2021)
Frontiers in Physiology, Vol 12 (2021)
Analysis of pulmonary function tests (PFTs) is an area where machine learning (ML) may benefit clinicians, researchers, and the patients. PFT measures spirometry, lung volumes, and carbon monoxide diffusion capacity of the lung (DLCO). The results ar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::682c9f130338abb7fca5d1ddff5a0ac0
https://pubmed.ncbi.nlm.nih.gov/34248665
https://pubmed.ncbi.nlm.nih.gov/34248665