A Machine Learning Approach to the Interpretation of Cardiopulmonary Exercise Tests: Development and Validation.
Journal
Pulmonary medicine
ISSN: 2090-1844
Titre abrégé: Pulm Med
Pays: Egypt
ID NLM: 101558762
Informations de publication
Date de publication:
2021
2021
Historique:
received:
10
02
2021
revised:
25
04
2021
accepted:
20
05
2021
entrez:
23
6
2021
pubmed:
24
6
2021
medline:
15
1
2022
Statut:
epublish
Résumé
At present, there is no consensus on the best strategy for interpreting the cardiopulmonary exercise test's (CPET) results. This study is aimed at assessing the potential of using computer-aided algorithms to evaluate CPET data for identifying chronic heart failure (CHF) and chronic obstructive pulmonary disease (COPD). Data from 234 CPET files from the Pulmonary Institute, at Sheba Medical Center, and the Givat-Washington College, both in Israel, were selected for this study. The selected CPET files included patients with confirmed primary CHF ( The disease classification results show that the overall predictive power of the proposed interpretive model ranged from 96% to 100%, indicating very high predictive power. Furthermore, the sensitivity, specificity, and overall precision of the proposed interpretive module were 99%, 99%, and 99%, respectively. The proposed new computer-aided CPET interpretive module was found to be highly sensitive and specific in classifying patients with CHF or COPD, or healthy. Comparable modules may well be applied to additional and larger populations (pathologies and exercise limitations), thereby making this tool powerful and clinically applicable.
Identifiants
pubmed: 34158976
doi: 10.1155/2021/5516248
pmc: PMC8188599
doi:
Types de publication
Journal Article
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
5516248Informations de copyright
Copyright © 2021 Or Inbar et al.
Déclaration de conflit d'intérêts
The authors declare that there are no conflicts of interest regarding the publication of this paper.
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