JARVISDHL: Transforming Chronic Care for Diabetes, Hypertension and Hyperlipidemia (DHL) with AI
Main Applicant – Professor Wynne Hsu
Institute of Data Science, National University of Singapore

This project aims to develop tools that help primary care teams stop or slow disease progression and complication development in Diabetes, Hypertension and HyperLipidemia (DHL) patients by 20% in 5 years.

The JARVISDHL projects seeks to prevent DHL in high risk healthy groups and reduce complications for existing DHL patients by the following: (1) enable predictive care for our DHL patients through early screening and risk stratification, (2) facilitate practice of evidence-based personalised care and shared-decision making by primary care physicians, and (3) empower patients to take ownership of their healthcare journey beyond the clinical visits through the use of technologies for patient education and self-care. The team had leveraged on the extensive data from SingHealth’s electronic medical records and registries to develop the AI methods and models for JARVISDHL. They had developed AI algorithms to enable early stratification of patients, quantify personalized treatment benefits and risk of complications and recommend evidence-based treatment options based on local medical data.

With the dataset from TRUST, the research team will be looking at:
– Validating of the existing models developed using SingHealth datasets
– Refining the developed models with the bigger dataset