The research interests of Yanshan Wang, Ph.D., focus on artificial intelligence (AI), natural language processing, and machine (or deep) learning methodologies and applications in clinical research, practice and education. His research goal is to use data-driven approaches to meet the needs of clinicians, researchers and, most importantly, patients.
- Informatics methods, tools, and infrastructures for cohort discovery. Dr. Wang aims to develop innovative informatics methods, tools and infrastructures to accelerate cohort discovery using electronic health records. Informatics techniques include natural language processing, information retrieval and deep learning. The specific clinical and translational application examples are the identification of patient cohorts for cohort studies and the recruitment of eligible patients for clinical trials.
- AI for clinical research using electronic health records. The rapid adoption of electronic health records systems has enabled the secondary use of the electronic health records data for developing advanced computational models using AI techniques. Dr. Wang is particularly interested in developing supervised and unsupervised machine (deep) learning models to discover new findings using the multimodal data stored in electronic health records such as structured data, clinical notes and images.
Significance to patient care
Dr. Wang's methodological work has the potential to discover new findings from electronic health records. These findings can enable better patient care and accelerate translational research to provide novel treatment to patients.