ClinicalTrials.gov
public full text
Public Domain
Safety and Feasibility of a Machine-Learning Bolus Priming Added to Existing Control Algorithm
A randomized crossover trial assessing glycemic control using Reinforcement Learning trained Bolus Priming System (BPS\_RL) added to the the Automated Insulin Delivery as Adaptive NETwork (AIDANET algorithm) compared to the original AIDANET algorithm.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- New Strategies for Postprandial Glycemic Control Using Insulin Pump Therapy#432818
- Short-Term Intensive Insulin Therapy Induction of Long-term Glycemic Control#391070
- Feasibility Study Assessing The Ability Of An Insulin Pump-Controlling Algorithm To Minimize Hypoglycemia And Hyperglycemia In Patients With Type 1 Diabetes In A Clinical Research Setting#1103508
- AI-Assisted Remote Insulin Pump Optimization in T1DM#1186260
- Phase 4 Study to Evaluate Treatment Optimization With Once-daily Insulin Glargine 300 U/mL#981896
Record · ID 1364965
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive