3 papers
cs.LG2026
SSMCGM-Stream: A Streaming State-Space Model for Personalized Diabetes Management
Shakson Isaac, Chirag Patel
Continuous glucose monitoring supports diabetes management but requires forecasting models that are accurate, interpretable, and efficient enough for real-time deployment. We prese…
cs.LG2025
SSM-CGM: Interpretable State-Space Forecasting Model of Continuous Glucose Monitoring for Personalized Diabetes Management
Shakson Isaac, Yentl Collin, Chirag Patel
Continuous glucose monitoring (CGM) generates dense data streams critical for diabetes management, but most used forecasting models lack interpretability for clinical use. We prese…
q-bio.QM2024
Barttender: An approachable & interpretable way to compare medical imaging and non-imaging data
Ayush Singla, Shakson Isaac, Chirag J. Patel
Imaging-based deep learning has transformed healthcare research, yet its clinical adoption remains limited due to challenges in comparing imaging models with traditional non-imagin…