Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network

被引：0
|
作者：
Sun, Qingnan [1 ]
Jankovic, Marko V. [1 ,2 ]
Bally, Lia [3 ,4 ]
Mougiakakou, Stavroula G. [1 ,3 ]
机构：
[1] Univ Bern, ARTORG Ctr Biomed Engn Res, Murtenstr 50, CH-3008 Bern, Switzerland
[2] Bern Univ Hosp, Dept Emergency Med, Inselspital, Bern, Switzerland
[3] Univ Bern, Bern Univ Hosp, Dept Diabet Endocrinol Clin Nutr & Metab, Inselspital, Bern, Switzerland
[4] Univ Bern, Bern Univ Hosp, Dept Gen Internal Med, Inselspital, Freiburgstr 15, CH-3010 Bern, Switzerland
来源：
2018 14TH SYMPOSIUM ON NEURAL NETWORKS AND APPLICATIONS (NEUREL) | 2018年
关键词：
blood glucose level; diabetes; prediction; long-short-term memory network;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A deep learning network was used to predict future blood glucose levels, as this can permit diabetes patients to take action before imminent hyperglycaemia and hypoglycaemia. A sequential model with one long-short-term memory (LSTM) layer, one bidirectional LSTM layer and several fully connected layers was used to predict blood glucose levels for different prediction horizons. The method was trained and tested on 26 retrospectively analysed datasets from 20 real patients. The proposed network outperforms the baseline methods in terms of all evaluation criteria.
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页数：5
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