A Continuous Multiple-Timestep Blood Glucose Level Prediction System Using Stacked LSTM for High-Accuracy Hypoglycemia Alerts in Smart Contact Lenses-Reference-Cited by-同舟云学术

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A Continuous Multiple-Timestep Blood Glucose Level Prediction System Using Stacked LSTM for High-Accuracy Hypoglycemia Alerts in Smart Contact Lenses

Published:2023-12-07 Issue: Volume: Page:109-110
ISSN:
Container-title:2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology
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Author:

Nagai Ryosuke¹,Inada Masaharu¹,Kitaike Hiroaki¹,Tagawa Hironori¹,Terauchi Mitsuru¹,Quan Tran Minh²,Nakamura Hiroaki³,Niitsu Kiichi¹

Affiliation:

1. Kyoto University,Japan

2. Hanoi University of Public Health,Vietnam

3. ShuhariSystem,Japan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10404092/10404107/10405113.pdf?arnumber=10405113

Reference5 articles.

1. Joint Hypoglycemia Prediction and Glucose Forecasting via Deep Multi-Task Learning

2. Personalized Blood Glucose Prediction for Type 1 Diabetes Using Evidential Deep Learning and Meta-Learning

3. A 385μm × 385μm 0.165V 0.27nW Fully-Integrated Supply-Modulated OOK CMOS TX in 65nm CMOS for Glasses-Free, Self-Powered, and Fuel-Cell-Embedded Continuous Glucose Monitoring Contact Lens;Hayashi

4. A 0.5-mm² Solar Cell-Powered Biofuel Cell-Input Biosensing System With LED Driving for Stand-Alone RF-Less Continuous Glucose Monitoring Contact Lens

5. AI-Based Edge-Intelligent Hypoglycemia Prediction System Using Alternate Learning and Inference Method for Blood Glucose Level Data with Low-periodicity

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