Conformance verification for neural network models of glucose-insulin dynamics-Reference-Cited by-同舟云学术

Conformance verification for neural network models of glucose-insulin dynamics

Published:2020-04-17 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control
language:
Short-container-title:

Author:

Kushner Taisa¹,Sankaranarayanan Sriram¹,Breton Marc²

Affiliation:

1. Univ. of Colorado Boulder

2. Univ. of Virginia

Funder

National Science Foundation

Juvenile Diabetes Research Foundation United States of America

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3365365.3382210

Reference72 articles.

1. Martín Abadi Ashish Agarwal Paul Barham and etal 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https://www.tensorflow.org/ Software available from tensorflow.org. Martín Abadi Ashish Agarwal Paul Barham and et al. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https://www.tensorflow.org/ Software available from tensorflow.org.

2. Probabilistic Temporal Logic Falsification of Cyber-Physical Systems

3. Yashwanth Annpureddy Che Liu Georgios E. Fainekos and Sriram Sankaranarayanan. 2011. S-TaLiRo: A Tool for Temporal Logic Falsification for Hybrid Systems. In TACAS. 254--257. Yashwanth Annpureddy Che Liu Georgios E. Fainekos and Sriram Sankaranarayanan. 2011. S-TaLiRo: A Tool for Temporal Logic Falsification for Hybrid Systems. In TACAS. 254--257.

4. Physiologic evaluation of factors controlling glucose tolerance in man: measurement of insulin sensitivity and beta-cell glucose sensitivity from the response to intravenous glucose.

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Digital twins and artificial intelligence in metabolic disease research;Trends in Endocrinology & Metabolism;2024-06

2. The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP;Scientific Reports;2023-10-06

3. Artificial Intelligence Based Prediction of Diabetic Foot Risk in Patients with Diabetes: A Literature Review;Applied Sciences;2023-02-22

4. Model Identification with Incomplete Input Data in Type 1 Diabetes*;IFAC-PapersOnLine;2023