My research focuses on developing machine learning and statistical methods to analyze multi-modality data for patient outcome modeling. These models can be used to personalize cancer patients’ treatment and improve their prognosis. We emphasize on interpretable AI in health care to understand the underlying biological mechanisms that contribute to the specific outcomes for different individuals to provide robust treatment assist for sequential decision making in the practice.
Accomplishments and Awards
- 2023 Propelling Original Data Science (PODS) Grant Award: Interpretable machine learning to identify tumor spatial features from longitudinal multi-modality images for personalized progression risk prediction of poor prognosis head and neck cancer