Fully automated insulin treatment

Fully automated insulin treatment

Citation Author(s):
Seunghyun
Lee
POSTECH
Submitted by:
Seunghyun Lee
Last updated:
Mon, 11/04/2019 - 03:07
DOI:
10.21227/z0nt-z923
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Abstract: 

This dataset contains in-silico results of insulin treatment using a fully automated artificial pancreas algorithm based on reinforcement learning for FDA-approved virtual patients (C. D. Man et al., 2014) with type 1 diabetes (10 adults and 10 adolescents). 

Instructions: 

The data contains the treatment results of the trained reinforcement learning based control algorithm over three different random seeds in each of the 20 virtual patients on seven runs of a single-meal (SM) scenario (randomly gernerated from mean amount of 65g with a standard deviation of 17g) following a preprandial fasting period with measurement noise. It also contains the treatment results of the trained algorihtm over three runs on two days of real life micking mult-meal (MM) scenario (40 g of CHO for breakfast at 8:00 a.m., 80 g of CHO for lunch at 1:00 p.m., and 60g of CHO for dinner at 9:00 p.m.). 

 

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[1] Seunghyun Lee, "Fully automated insulin treatment", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/z0nt-z923. Accessed: Nov. 19, 2019.
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doi = {10.21227/z0nt-z923},
url = {http://dx.doi.org/10.21227/z0nt-z923},
author = {Seunghyun Lee },
publisher = {IEEE Dataport},
title = {Fully automated insulin treatment},
year = {2019} }
TY - DATA
T1 - Fully automated insulin treatment
AU - Seunghyun Lee
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Seunghyun Lee. (2019). Fully automated insulin treatment. IEEE Dataport. http://dx.doi.org/10.21227/z0nt-z923
Seunghyun Lee, 2019. Fully automated insulin treatment. Available at: http://dx.doi.org/10.21227/z0nt-z923.
Seunghyun Lee. (2019). "Fully automated insulin treatment." Web.
1. Seunghyun Lee. Fully automated insulin treatment [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/z0nt-z923
Seunghyun Lee. "Fully automated insulin treatment." doi: 10.21227/z0nt-z923