Researchers at the Center for Artificial Intelligence Driven Health Data Systems and Analytics are working to utilize the power of data analytics, machine learning, high-performance computation, and well-curated datasets to advance solutions to the most prominent world health disorders. This is accomplished through a research ecosystem that allows faculty and partners to work together on the range of issues related to the disorders. This unified, efficient, and powerful data platform that helps aggregate and harmonize datasets from variety of healthcare partners and provides extensive capabilities and resources for the teams to investigate the disorders.
Current Research Projects Include
- Single-cell breast cancer therapeutics: This project will use model-based unsupervised machine learning and game theoretic methods to infer drug response in triple-negative breast cancer using single-cell RNA-seq.
- Antidepressant response in major depressive disorder: This project will use supervised machine learning methods to predict drug responses in depression patients treated with antidepressants using metabolomics, genomics, and clinical data.
- Predicting surgical readmissions in diabetic patients: This project uses factor graphs to model longitudinal relationships between comorbidity of diabetic patients that likely lead to surgical readmissions in the future. This project uses electronic health record data from diabetic patients at the National University Hospital, Singapore.
- Prediction of Alzheimer’s disease progression: This project uses multi-modal data with supervised learning to understand contribution of different data modalities in the progression of Alzheimer’s disease.
- Seizure onset localization: This project uses time-series iEEG data with graphical models to capture spatial-temporal dependencies that contribute to determining the onset of seizures.
- Understanding pathophysiology of dementia: This project uses deep learning techniques to understand pathophysiology of dementia.