Physician-Friendly Machine Learning: A Case Study with Cardi…
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- DOI 10.3390/jcm8071050
- PMID https://pubmed.ncbi.nlm.nih.gov/31323843
- PMCID https://www.ncbi.nlm.nih.gov/pmc/articles/6678298
- OpenAlex ID W2961085424
- Type Article
- Language EN
- Published 18 Jul 2019
- Volume 8 (7)
- Pages 1050
- Source Journal of Clinical Medicine
- Publisher Multidisciplinary Digital Publishin…
- FWCI 9.86
- Countries 1
- Institutions 1
- Added 23 Jul 2019
- Updated 28 Oct 2026
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Physician-Friendly Machine Learning: A Case Study with Cardiovascular Disease Risk Prediction
Article 2019 EN Gold
Machine learning is often perceived as a sophisticated technology accessible only by highly trained experts. This prevents many physicians and biologists from using this tool in their research. The goal of this paper is to eliminate this out-dated perception. We argue that the recent development of auto machine learning techniques enables biomedical researchers to quickly build competitive machine…
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9.86
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UN Sustainable Development Goals
1 SDGsAuthors & Affiliations
4 authors| # | Author | Institutions | ORCID |
|---|---|---|---|
| 1 | Meghana Padmanabhan | University of Houston (US) | — |
| 2 | Pengyu Yuan | University of Houston (US) | |
| 3 | Govind Chada | University of Houston (US) | — |
| 4 | Hien Van Nguyen Corresponding | University of Houston (US) |
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