# Short: Basal-Adjust: Trend Prediction Alerts and Adjusted Basal Rates for Hyperglycemia Prevention

arXiv: https://arxiv.org/abs/2303.09913 · HTML: https://mlsanity.com/paper/2303.09913/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 16 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Design and Validation of an Open-Source Closed-Loop Testbed for Artificial Pancreas Systems](https://mlsanity.com/paper/2208.06479/) (2022)
- [Data-driven Design of Context-aware Monitors for Hazard Prediction in Artificial Pancreas Systems](https://mlsanity.com/paper/2104.02545/) (2021)
- [Robustness Testing of Data and Knowledge Driven Anomaly Detection in Cyber-Physical Systems](https://mlsanity.com/paper/2204.09183/) (2022)

## Built on by (most cited first, 1 of 1)

- [Integrating Neural Differential Forecasting with Safe Reinforcement Learning for Blood Glucose Regulation](https://mlsanity.com/paper/2511.12417/) (2025)

## Abstract (arXiv)

Significant advancements in type 1 diabetes treatment have been made in the development of state-of-the-art Artificial Pancreas Systems (APS). However, lapses currently exist in the timely treatment of unsafe blood glucose (BG) levels, especially in the case of rebound hyperglycemia. We propose a machine learning (ML) method for predictive BG scenario categorization that outputs messages alerting the patient to upcoming BG trends to allow for earlier, educated treatment. In addition to standard notifications of predicted hypoglycemia and hyperglycemia, we introduce BG scenario-specific alert messages and the preliminary steps toward precise basal suggestions for the prevention of rebound hyperglycemia. Experimental evaluation on the DCLP3 clinical dataset achieves >98% accuracy and >79% precision for predicting rebound high events for patient alerts.