11 papers
Counterfactual Modeling with Fine-Tuned LLMs for Health Intervention Design and Sensor Data Augmentation
Shovito Barua Soumma, Asiful Arefeen, Stephanie M. Carpenter +2
Counterfactual explanations (CFEs) provide human-centric interpretability by identifying the minimal, actionable changes required to alter a machine learning model's prediction. Th…
MetaPlate: Counterfactual-Guided RAG-LLM Tool for Personalized Food Recommendation and Hyperglycemia Prevention
Asiful Arefeen, Carol Johnston, Hassan Ghasemzadeh
Postprandial hyperglycemia is a key risk factor for metabolic disorders; however, existing dietary guidance is often static, impractical, and insufficiently personalized, providing…
GlyTwin: Digital Twin for Glucose Control in Type 1 Diabetes Through Optimal Behavioral Modifications Using Patient-Centric Counterfactuals
Asiful Arefeen, Saman Khamesian, Maria Adela Grando +2
Frequent and long-term exposure to hyperglycemia increases the risk of chronic complications, including neuropathy, nephropathy, and cardiovascular disease. Existing continuous sub…
Glycemic-Aware and Architecture-Agnostic Training Framework for Blood Glucose Forecasting in Type 1 Diabetes
Saman Khamesian, Asiful Arefeen, Maria Adela Grando +2
Managing Type 1 Diabetes (T1D) demands constant vigilance as individuals strive to regulate their blood glucose levels and avoid dysglycemia, including hyperglycemia and hypoglycem…
SenseCF: LLM-Prompted Counterfactuals for Intervention and Sensor Data Augmentation
Shovito Barua Soumma, Asiful Arefeen, Stephanie M. Carpenter +2
Counterfactual explanations (CFs) offer human-centric insights into machine learning predictions by highlighting minimal changes required to alter an outcome. Therefore, CFs can be…
RealAC: A Domain-Agnostic Framework for Realistic and Actionable Counterfactual Explanations
Asiful Arefeen, Shovito Barua Soumma, Hassan Ghasemzadeh
Counterfactual explanations provide human-understandable reasoning for AI-made decisions by describing minimal changes to input features that would alter a model's prediction. To b…