5 papers
Informative Missingness to Generate Irregular Clinical Time Series
Hadi Mehdizavareh, Gabriele Santangelo, Giovanna Nicora +4
Laboratory tests in electronic health records are collected irregularly, and the absence of a test order can be as informative as the measurement itself. Such missingness reflects…
Tipping the Balance: Impact of Class Imbalance Correction on the Performance of Clinical Risk Prediction Models
Amalie Koch Andersen, Hadi Mehdizavareh, Arijit Khan +8
Objective: ML-based clinical risk prediction models are increasingly used to support decision-making in healthcare. While class-imbalance correction techniques are commonly applied…
Personalized Forecasting of Glycemic Control in Type 1 and 2 Diabetes Using Foundational AI and Machine Learning Models
Simon Lebech Cichosz, Stine Hangaard, Thomas Kronborg +2
Background: Accurate week-ahead forecasts of continuous glucose monitoring (CGM) derived metrics could enable proactive diabetes management, but relative performance of modern tabu…
Peak-Nadir Encoding for Efficient CGM Data Compression and High-Fidelity Reconstruction
Clara Bender, Line Davidsen, Søren Schou Olesen +1
Aim/background: Continuous glucose monitoring (CGM) generates dense time-series data, posing challenges for efficient storage, transmission, and analysis. This study evaluates nove…
Enhancing Glucose Level Prediction of ICU Patients through Hierarchical Modeling of Irregular Time-Series
Hadi Mehdizavareh, Arijit Khan, Simon Lebech Cichosz
Accurately predicting blood glucose (BG) levels of ICU patients is critical, as both hypoglycemia (BG < 70 mg/dL) and hyperglycemia (BG > 180 mg/dL) are associated with increased m…