Using Machine Learning to Identify Organ System Specific Limitations to Exercise via Cardiopulmonary Exercise Testing-Reference-Cited by-同舟云学术

Using Machine Learning to Identify Organ System Specific Limitations to Exercise via Cardiopulmonary Exercise Testing

Published:2022-08 Issue:8 Volume:26 Page:4228-4237
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Portella Julio J.¹^ORCID,Andonian Brian J.²^ORCID,Brown Donald E.³^ORCID,Mansur Joao⁴,Wales Derek⁵,West Vivian L.⁶^ORCID,Kraus William E.²^ORCID,Hammond William Ed⁶

Affiliation:

1. Center for Health Informatics, Duke University, Durham, NC, USA

2. Duke Molecular Physiology Institute, Duke University, Durham, NC, USA

3. Integrated Translational Health Research Institute, University of Virginia, Charlottesville, VA, USA

4. T-Mobile, Durham, NC, USA

5. Fuqua School of Business, Duke University, Durham, NC, USA

6. Duke Center for Health Informatics, CTSI, Duke University, Durham, NC, USA

Funder

Duke University Master in Interdisciplinary Data Science Program

NIH

Duke Pepper Center REC Career Development Award

Rauch Family Research Scholarship

Georgia Clinical and Translational Science Alliance

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/9855434/09745349.pdf?arnumber=9745349

Reference34 articles.

1. Comparision of diabetic prediction AutoML model with customized model

2. An introduction to ROC analysis

3. Patterns of Oxygen Pulse Curve in Response to Incremental Exercise in Patients with Chronic Obstructive Pulmonary Disease – An Observational Study

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