An Artificial Intelligence-Based System for Nutrient Intake Assessment of Hospitalised Patients

no code implementations • 7 Jun 2019 • Ya Lu, Thomai Stathopoulou, Maria F. Vasiloglou, Stergios Christodoulidis, Beat Blum, Thomas Walser, Vinzenz Meier, Zeno Stanga, Stavroula G. Mougiakakou

Regular nutrient intake monitoring in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition (DRM).

A dual mode adaptive basal-bolus advisor based on reinforcement learning

no code implementations • 7 Jan 2019 • Qingnan Sun, Marko V. Jankovic, João Budzinski, Brett Moore, Peter Diem, Christoph Stettler, Stavroula G. Mougiakakou

Self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) are commonly used by type 1 diabetes (T1D) patients to measure glucose concentrations.

reinforcement-learning Reinforcement Learning (RL)

Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network

no code implementations • 11 Sep 2018 • Qingnan Sun, Marko V. Jankovic, Lia Bally, Stavroula G. Mougiakakou

A deep learning network was used to predict future blood glucose levels, as this can permit diabetes patients to take action before imminent hyperglycaemia and hypoglycaemia.