Zhao et al., 2018 - Google Patents
Multiple order model migration and optimal model selection for online glucose prediction in type 1 diabetesZhao et al., 2018
- Document ID
- 13573413412075965797
- Author
- Zhao H
- Zhao C
- Yu C
- Dassau E
- Publication year
- Publication venue
- AIChE Journal
External Links
Snippet
To address the problem of insufficient available modeling data for glucose prediction, as well as modeling burden, a model migration method was developed in a previous work to quickly transfer an old model to a new subject using a simple parameter adjustment …
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Di Ferdinando et al. | Semiglobal sampled-data dynamic output feedback controller for the glucose–insulin system | |
Fong et al. | Using causality modeling and Fuzzy Lattice Reasoning algorithm for predicting blood glucose | |
Balakrishnan et al. | A deep analysis on optimization techniques for appropriate PID tuning to incline efficient artificial pancreas | |
Sun et al. | Prior informed regularization of recursively updated latent-variables-based models with missing observations | |
Kim et al. | Intelligent ensemble deep learning system for blood glucose prediction using genetic algorithms | |
Dua et al. | Multi-objective blood glucose control for type 1 diabetes | |
Baucum et al. | Adapting reinforcement learning treatment policies using limited data to personalize critical care | |
Gómez-Castillo et al. | A machine learning approach for blood glucose level prediction using a LSTM network | |
Li et al. | Chaotic time series analysis approach for prediction blood glucose concentration based on echo state networks | |
Parra et al. | Learning Difference Equations with Structured Grammatical Evolution for Postprandial Glycaemia Prediction | |
Cai et al. | Data-enabled learning and control algorithms for intelligent glucose management: the state of the art | |
Luo et al. | DTR-Bench: An in silico Environment and Benchmark Platform for Reinforcement Learning Based Dynamic Treatment Regime | |
Bosoni et al. | Deep learning applied to blood glucose prediction from flash glucose monitoring and fitbit data |