A Machine Learning Approach to the Interpretation of Cardiopulmonary Exercise Tests: Development and Validation.

Journal: Pulmonary medicine

PMID: 34158976

Abstract

OBJECTIVE: 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).

Authors

Or Inbar

Department of Biomedical Engineering, Tel-Aviv University, Israel.
Omri Inbar

The Edmond and Lily Safra Children's Hospital, Sheba Medical Center, Tel Hashomer, Israel.
Ronen Reuveny

Pulmonary Institute, Sheba Medical Center Tel-Hashomer, Israel.
Michael J Segel

Pulmonary Institute, Sheba Medical Center Tel-Hashomer, Israel.
Hayit Greenspan
Mickey Scheinowitz

Department of Biomedical Engineering, Tel-Aviv University, Israel.

Keywords

Adult Aged Chronic Disease Exercise Test Female Heart Failure Humans Machine Learning Male Middle Aged Predictive Value of Tests Pulmonary Disease, Chronic Obstructive Respiratory Function Tests Retrospective Studies Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (34158976)

A Machine Learning Approach to the Interpretation of Cardiopulmonary Exercise Tests: Development and Validation.

Abstract

Authors

Keywords

External Resources

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