| Autor: |
Mhaskar, H. N., Pereverzyev, S. V., van der Walt, M. D. |
| Rok vydání: |
2017 |
| Předmět: |
|
| Zdroj: |
Front. Appl. Math. Stat., 14 July 2017 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.3389/fams.2017.00014 |
| Popis: |
We consider the question of 30-minute prediction of blood glucose levels measured by continuous glucose monitoring devices, using clinical data. While most studies of this nature deal with one patient at a time, we take a certain percentage of patients in the data set as training data, and test on the remainder of the patients; i.e., the machine need not re-calibrate on the new patients in the data set. We demonstrate how deep learning can outperform shallow networks in this example. One novelty is to demonstrate how a parsimonious deep representation can be constructed using domain knowledge. |
| Databáze: |
arXiv |
| Externí odkaz: |
|
