Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine
Authors: Ilker Demirel, Ahmet Alparslan Celik, Cem Tekin
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we make in silico experiments on the bolus-insulin dose allocation problem in type-1 diabetes mellitus disease and compare our algorithms against the famous GP-UCB algorithm, the rule-based dose calculators, and a clinician. |
| Researcher Affiliation | Academia | Ilker Demirel Bilkent University EMAIL. Alparslan Celik Bilkent University EMAIL Tekin Bilkent University EMAIL |
| Pseudocode | Yes | Algorithm 1 ESCADA algorithm |
| Open Source Code | Yes | Our code is available at https://github.com/Bilkent-CYBORG/ESCADA. |
| Open Datasets | Yes | We make in silico experiments using the open-source implementation [57] of the U.S. FDA approved University of Virginia (UVA)/PADOVA T1DM simulator [23] |
| Dataset Splits | No | The paper describes how data is generated through simulation (e.g., 'We make 15 consecutive dose recommendations for a meal event in a single run. We repeat this experiment with 30 different meal events for all 30 patients.'), but it does not specify explicit training, validation, or test dataset splits in terms of percentages or counts for a pre-existing dataset. |
| Hardware Specification | Yes | Appendix E: We use a workstation with Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz with 8GB of RAM, and NVIDIA GeForce GTX 1080 GPU. |
| Software Dependencies | Yes | Appendix E: We use Python 3.7.4. The following libraries with their versions are used: numpy==1.18.5, scipy==1.5.0, scikit-learn==0.23.1, GPy==1.9.9, matplotlib==3.2.2. Simglucose v0.2.1. |
| Experiment Setup | Yes | We set the target blood glucose (BG) level to 112.5 mg/dl [22]. We sample carbohydrate intake for each meal event from [20, 80] g, and fasting blood glucose from [100, 150] mg/dl. We make 15 consecutive dose recommendations for a meal event in a single run. We repeat this experiment with 30 different meal events for all 30 patients. |