On the Predictability of non-CGM Diabetes Data for Personalized Recommendation
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Preprint |
| Publié: |
2018
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866929306433748992 |
|---|---|
| author | Nguyen, Tu Rokicki, Markus |
| author_facet | Nguyen, Tu Rokicki, Markus |
| contents | With continuous glucose monitoring (CGM), data-driven models on blood glucose prediction have been shown to be effective in related work. However, such (CGM) systems are not always available, e.g., for a patient at home. In this work, we conduct a study on 9 patients and examine the online predictability of data-driven (aka. machine learning) based models on patient-level blood glucose prediction; with measurements are taken only periodically (i.e., after several hours). To this end, we propose several post-prediction methods to account for the noise nature of these data, that marginally improves the performance of the end system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1808_07380 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | On the Predictability of non-CGM Diabetes Data for Personalized Recommendation Nguyen, Tu Rokicki, Markus Computers and Society Machine Learning With continuous glucose monitoring (CGM), data-driven models on blood glucose prediction have been shown to be effective in related work. However, such (CGM) systems are not always available, e.g., for a patient at home. In this work, we conduct a study on 9 patients and examine the online predictability of data-driven (aka. machine learning) based models on patient-level blood glucose prediction; with measurements are taken only periodically (i.e., after several hours). To this end, we propose several post-prediction methods to account for the noise nature of these data, that marginally improves the performance of the end system. |
| title | On the Predictability of non-CGM Diabetes Data for Personalized Recommendation |
| topic | Computers and Society Machine Learning |
| url | https://arxiv.org/abs/1808.07380 |