Aziz Guergachi

Identifying Prediabetes in Canadian Populations Using Machine Learning

Predicting Time to Diabetes Diagnosis Using Random Survival Forests

Predicting Diabetes in Canadian Adults Using Machine Learning

Towards a Regulatory Framework for Workflow Improvement in Electronic Medical Records

Exploring Prediabetes Pathways Using Explainable AI on Data from Electronic Medical Records

A Framework for Implementing Disease Prevention and Behavior Change Evidence at Scale

Measuring and Managing Healthcare Supply and Demand in Real-Time

Validation of a Design Architecture to Deliver Health Management and Behavior Change Evidence at Scale

Artificial intelligence with temporal features outperforms machine learning in predicting diabetes

Applying Patient Segmentation Using Primary Care Electronic Medical Records to Develop a Virtual Peer-to-Peer Intervention for Patients with Type 2 Diabetes

Characterization of Inclination Analysis for Predicting Onset of Heart Failure from Primary Care Electronic Medical Records

Multi-Input Multi-Output Dynamic Modelling of Type 2 Diabetes Progression

A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations

High cardiovascular disease risk-associated with the incidence of Type 2 diabetes among prediabetics

Characterization of Type 2 Diabetes Using Counterfactuals and Explainable AI

Behavioral Segmentation for Enhanced Peer-to-Peer Patient Education

An Online Risk Tool for Predicting Type 2 Diabetes Mellitus

Handling Irregularly Sampled Longitudinal Data and Prognostic Modeling of Diabetes Using Machine Learning Technique

A Hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression

Predictive models for diabetes mellitus using machine learning techniques

Prognostic Modeling and Prevention of Diabetes Using Machine Learning Technique

Metabolic Syndrome and Development of Diabetes Mellitus: Predictive Modeling Based on Machine Learning Techniques

A Systematic Machine Learning Based Approach for the Diagnosis of Non-Alcoholic Fatty Liver Disease Risk and Progression

Application of the convolution operator for scenario integration with loss data in operational risk modeling