https://github.com/samankhamesian/guide

We present GUIDE (Glucose Intelligence Decision Engine), an RL-based framework that generates patient-specific recommendations for insulin and carbohydrate intake, adapting to individual glucose dynamics and daily routines
https://github.com/samankhamesian/guide

automated-insulin-delivery continuous-glucose-monitoring cql-algorithm ppo reinforcement-learning sac simulation td3-bc type-1-diabetes

Last synced: 3 months ago
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We present GUIDE (Glucose Intelligence Decision Engine), an RL-based framework that generates patient-specific recommendations for insulin and carbohydrate intake, adapting to individual glucose dynamics and daily routines

• Host: GitHub
• URL: https://github.com/samankhamesian/guide
• Owner: SamanKhamesian
• Created: 2025-06-21T02:49:50.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2026-04-02T19:24:28.000Z (3 months ago)
• Last Synced: 2026-04-03T06:38:51.897Z (3 months ago)
• Topics: automated-insulin-delivery, continuous-glucose-monitoring, cql-algorithm, ppo, reinforcement-learning, sac, simulation, td3-bc, type-1-diabetes
• Language: Python
• Homepage:
• Size: 136 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# GUIDE

**GUIDE: Reinforcement Learning for Behavioral Action Support in Type 1 Diabetes**

Type 1 Diabetes (T1D) management requires continuous adjustment of insulin and lifestyle behaviors to maintain blood glucose within a safe target range. 

Although automated insulin delivery (AID) systems have improved glycemic outcomes, many patients still fail to achieve recommended clinical targets, warranting new approaches to improve glucose control in patients with T1D. 

While reinforcement learning (RL) has been utilized as a promising approach, current RL-based methods focus primarily on insulin-only treatment and do not provide behavioral recommendations for glucose control. 

To address this gap, we propose GUIDE, an RL-based decision-support framework designed to complement AID technologies by providing behavioral recommendations to prevent abnormal glucose events. 

GUIDE generates structured actions defined by intervention type, magnitude, and timing, including bolus insulin administration and carbohydrate intake events. 

GUIDE integrates a patient-specific glucose level predictor trained on real-world continuous glucose monitoring data and supports both offline and online RL algorithms within a unified environment. 

We evaluate both off-policy and on-policy methods across 25 individuals with T1D using standardized glycemic metrics. Among the evaluated approaches, the CQL-BC algorithm demonstrates the highest average time-in-range, reaching 85.49% while maintaining low hypoglycemia exposures. Behavioral similarity analysis further indicates that the learned CQL-BC policy preserves key structural characteristics of patient action patterns, achieving a mean cosine similarity of 0.87 ± 0.09 across subjects. 

These findings suggest that conservative offline RL with a structured behavioral action space can provide clinically meaningful and behaviorally plausible decision support for personalized diabetes management.

---

## 📁 Dataset Setup

### AZT1D Dataset

- **Download from Mendeley:**  

  https://data.mendeley.com/datasets/gk9m674wcx/1

- **Directory structure:**  

  Place ```AZT1D``` folder in the ```./GUIDE/dataset/``` directory:

---

## ⚙️ Environment Setup

- **Python version:** `3.12`

- **Install dependencies:**

  Create a virtual environment (optional but recommended):

  ```bash

  python -m venv venv

  source venv/bin/activate  # On Windows: venv\Scripts\activate

  ```

  Then install required packages:

  ```bash

  pip install -r requirements.txt

  ```

  `requirements.txt` includes:

  ```

  matplotlib==3.10.8

  numpy==2.4.2

  pandas==3.0.1

  scikit_learn==1.8.0

  scipy==1.17.1

  statsmodels==0.14.6

  tensorflow==2.20.0

  torch==2.7.0

  ```

---

## 📖 Citation

If you use GLIMMER in your work, please cite:

```bibtex

@misc{khamesian2026guidereinforcementlearningbehavioral,

      title={GUIDE: Reinforcement Learning for Behavioral Action Support in Type 1 Diabetes}, 

      author={Saman Khamesian and Sri Harini Balaji and Di Yang Shi and Stephanie M. Carpenter and Daniel E. Rivera and W. Bradley Knox and Peter Stone and Hassan Ghasemzadeh},

      year={2026},

      eprint={2604.00385},

      archivePrefix={arXiv},

      primaryClass={cs.LG},

      url={https://arxiv.org/abs/2604.00385}, 

}

```

---