实验中的超参数
1型糖尿病闭环人工胰腺DRL实验的超参数数据集，包含PPO算法技巧及安全机制设置。
基本信息
资源简介
实验中的超参数
【数据集背景】
本数据集源站标题为《Hyperparameters in the experiments.》。源站首次发布：2025-01-27。
【数据内容】
源站原始描述：Patients with type 1 diabetes and their physicians have long desired a fully closed-loop artificial pancreas (AP) system that can alleviate the burden of blood glucose regulation. Although deep reinforcement learning (DRL) methods theoretically enable adaptive insulin dosing control, they face numerous challenges, including safety and training efficiency, which have hindered their clinical application. This paper proposes a safe and efficient adaptive insulin delivery controller based on DRL. It…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 1.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0317662.t001。
【主题与分类】
源站学科分类：Space Science、Cell Biology、Biotechnology、Chemical Sciences not elsewhere classified、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified。主题标签：xlink "> patients、type 1 diabetes、simglucose simulator demonstrate、proximal policy optimization、proposed controller achieved、loop artificial pancreas、face numerous challenges、encouraging results indicate、employed ten tricks、blood glucose regulation。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Hyperparameters_in_the_experiments_/28288677
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Zhao, Yan Feng; Chaw, Jun Kit; Ang, Mei Choo; Tew, Yiqi; Shi, Xiao Yang; Liu, Lin; et al. (2025). Hyperparameters in the experiments.. PLOS ONE. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Hyperparameters in the experiments.
- DOI：10.1371/journal.pone.0317662.t001
- 发布日期：2025-01-27
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Hyperparameters_in_the_experiments_/28288677
命令行下载
curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/51958770"
文件清单
Table 1.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Patients with type 1 diabetes and their physicians have long desired a fully closed-loop artificial pancreas (AP) system that can alleviate the burden of blood glucose regulation. Although deep reinforcement learning (DRL) methods theoretically enable adaptive insulin dosing control, they face numerous challenges, including safety and training efficiency, which have hindered their clinical application. This paper proposes a safe and efficient adaptive insulin delivery controller based on DRL. It employed ten tricks to enhance the proximal policy optimization (PPO) algorithm, improving training efficiency. Additionally, a dual safety mechanism of ’proactive guidance + reactive correction’ was introduced to reduce the risks of hyperglycemia and hypoglycemia and to prevent emergencies. Performance evaluations in the Simglucose simulator demonstrate that the proposed controller achieved an 87.45% time in range (TIR) median, superior to baseline methods, with a lower incidence of hypoglycemia, notably eliminating severe hypoglycemia and treatment failures. These encouraging results indicate that the DRL-based fully closed-loop AP controller has taken an essential step toward clinical implementation.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0317662.t001（source: https://plos.figshare.com/articles/dataset/Hyperparameters_in_the_experiments_/28288677）。
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