# CrossGP: Cross-Day Glucose Prediction Excluding Physiological Information

arXiv: https://arxiv.org/abs/2404.10901 · HTML: https://mlsanity.com/paper/2404.10901/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 13 of 13)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Cross-Entropy Loss Functions: Theoretical Analysis and Applications](https://mlsanity.com/paper/2304.07288/) (2023)
- [Fastformer: Additive Attention Can Be All You Need](https://mlsanity.com/paper/2108.09084/) (2021)
- [DASGIL: Domain Adaptation for Semantic and Geometric-aware Image-based Localization](https://mlsanity.com/paper/2010.00573/) (2020)
- [FT2TF: First-Person Statement Text-To-Talking Face Generation](https://mlsanity.com/paper/2312.05430/) (2023)
- [Convolutional Recurrent Neural Networks for Glucose Prediction](https://mlsanity.com/paper/1807.03043/) (2018)
- [Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection](https://mlsanity.com/paper/2402.10862/) (2024)
- [AV-MaskEnhancer: Enhancing Video Representations through Audio-Visual Masked Autoencoder](https://mlsanity.com/paper/2309.08738/) (2023)
- [SAIC: Integration of Speech Anonymization and Identity Classification](https://mlsanity.com/paper/2312.15190/) (2023)
- [Blood Glucose Level Prediction: A Graph-based Explainable Method with Federated Learning](https://mlsanity.com/paper/2312.12541/) (2023)

## Built on by (most cited first, 2 of 2)

- [GluMarker: A Novel Predictive Modeling of Glycemic Control Through Digital Biomarkers](https://mlsanity.com/paper/2404.12605/) (2024)
- [Toward Short-Term Glucose Prediction Solely Based on CGM Time Series](https://mlsanity.com/paper/2404.11924/) (2024)

## Abstract (arXiv)

The increasing number of diabetic patients is a serious issue in society today, which has significant negative impacts on people's health and the country's financial expenditures. Because diabetes may develop into potential serious complications, early glucose prediction for diabetic patients is necessary for timely medical treatment. Existing glucose prediction methods typically utilize patients' private data (e.g. age, gender, ethnicity) and physiological parameters (e.g. blood pressure, heart rate) as reference features for glucose prediction, which inevitably leads to privacy protection concerns. Moreover, these models generally focus on either long-term (monthly-based) or short-term (minute-based) predictions. Long-term prediction methods are generally inaccurate because of the external uncertainties that can greatly affect the glucose values, while short-term ones fail to provide timely medical guidance. Based on the above issues, we propose CrossGP, a novel machine-learning framework for cross-day glucose prediction solely based on the patient's external activities without involving any physiological parameters. Meanwhile, we implement three baseline models for comparison. Extensive experiments on Anderson's dataset strongly demonstrate the superior performance of CrossGP and prove its potential for future real-life applications.