Asset Details
- Identifier:
- dmrr3348-fig-0001
- Description:
- Patient 1: Comparison of results of all methods. Comparison of the results for patient 1, with KRR linear with regular label, KRR RBF with regular label, KRR linear with special label, KRR RBF with special label. The real glucose levels are shown in red; the predicted glucose levels are shown in blue. KRR, kernel ridge regression; RBF, radial basis function
- License:
- Rights Managed
- Rights Holder:
- John Wiley & Sons, Inc.
- License Rights Holder:
- © 2020 John Wiley & Sons, Ltd.
- Asset Type:
- Image
- Asset Subtype:
- Chart/Graph
- Image Orientation:
- Landscape
- Image Dimensions:
- 2128 x 1282
- Image File Size:
- 820 KB
- Creator:
- Yonit Marcus, Roy Eldor, Mariana Yaron, Sigal Shaklai, Maya Ish‐Shalom, Gabi Shefer, Naftali Stern, Nehor Golan, Amit Z. Dvir, Ofir Pele, Mira Gonen
- Credit:
- Marcus, Y., Eldor, R., & Yaron, M. (2020). Improving blood glucose level predictability using machine learning. Diabetes/Metabolism Research and Reviews, 36(8), n/a. https://doi.org/10.1002/dmrr.3348.
- Collection:
- Keywords:
- Restrictions:
- Property Release:
- No
- Model Release:
- No
- Purchasable:
- Yes
- Sensitive Materials:
- No
- Article Authors:
- Yonit Marcus, Roy Eldor, Mariana Yaron, Sigal Shaklai, Maya Ish‐Shalom, Gabi Shefer, Naftali Stern, Nehor Golan, Amit Z. Dvir, Ofir Pele, Mira Gonen
- Article Copyright Year:
- 2020
- Publication Title:
- Diabetes/Metabolism Research and Reviews
- Publication Volume:
- 36
- Publication Issue:
- 8
- Publication Date:
- 11/01/2020
- DOI:
- https://doi.org/10.1002/dmrr.3348
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