MealMeter is a linear regression based technique applied on multi-modal data collected using a CGM sensor and a wristband and tracks meal macronutrients. MealMeter achieves as low as 0.37 average root mean squared relative errors (RMSRE) in carb tracking, which is at least 15.9% improvement compared to TabPFN foundational model and other baselines.
LEAD is an explainable AI technique that determines relative feature contributions by characterizing the decision boundary and perturbing critical samples along the decision boundary close to the test sample. LEAD achieves at least 6% improved fidelity and 7% improved consistency compared to LIME and SHAP.
This method reduces the labeling cost to train an efficient multi-task neural network by adding an additional clustering step in the multi-task active learning loop, within the sampling process
Glyman incorporates stakeholders' choices in producing counterfactual explanations to reduce the number of abnormal glycemic events T1D patients encounter.
GlyCoach presents a novel approach to charaterize the decision boundary of a model and leverage it to generate counterfactual expalantions reflecting user preferences.
Accurately forecasts future blood glucose level and predicts dysglycemic events thereby.
GlySim accurately forecasts future blood glucose level, intervenes by identifying the factor contributing most towards blood glucose spike and suggests optimal behavioral change to avoid abnormality.
With a Tiramisu model, we have been able to denoise ECG signals buried under motion noise and estimate inter-beat-intervals (IBI) accurately.
Briefly evaluates the factors behind medication adherence of patients at risk for atherosclerotic cardiovascular disease.
Optimizes change in consumed nutrients while driving users to their desired diet goals.
