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Title:

Using Machine Learning-Based Algorithms to Identify and Quantify Exercise Limitations in Clinical Practice: Are We There Yet?

Document type:
Article; Journal Article
Author(s):
Schwendinger, Fabian; Biehler, Ann-Kathrin; Nagy-Huber, Monika; Knaier, Raphael; Roth, Volker; Dumitrescu, Daniel; Meyer, F Joachim; Hager, Alfred; Schmidt-Trucksäss, Arno
Abstract:
INTRODUCTION: Well-trained staff is needed to interpret cardiopulmonary exercise tests (CPET). We aimed to examine the accuracy of machine learning-based algorithms to classify exercise limitations and their severity in clinical practice compared with expert consensus using patients presenting at a pulmonary clinic. METHODS: This study included 200 historical CPET data sets (48.5% female) of patients older than 40 yr referred for CPET because of unexplained dyspnea, preoperative examination, and...     »
Journal title abbreviation:
Med Sci Sports Exerc
Year:
2024
Journal volume:
56
Journal issue:
2
Pages contribution:
159-169
Fulltext / DOI:
doi:10.1249/MSS.0000000000003293
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/37703323
Print-ISSN:
0195-9131
TUM Institution:
Klinik für Kinderkardiologie und angeborene Herzfehler (DHM) (Prof. Ewert)
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