Our research lab is primarily interested in developing and applying computational approaches to biomedical text data and medical images (aka natural language processing and medical image analysis). It is motivated by the integration of clinical-inspired approaches to machine learning and, reciprocally, the use of these approaches to better understand decision-making in clinical systems. The following three goals represent possible directions for our future research:
- Informatics-empowered diagnostics and prognosis assistance.
- Patient-centered multimodal data processing and mining.
- AI technology for unstructured biomedical text.
Taken together, we expect our long-term research will have important impacts by allowing medical personnel to consider different dimensions of clinical/scientific data to find the best viable treatment methods for a complex medical condition. This will improve diagnostic performance, provide consistent recommendations for follow-up, and ultimately assist the creation of high-quality information services relevant to public health.
We welcome collaboration with other labs and investigators that seek to better understand biomedical literature, clinical narratives and images, and machine learning in medicine.