Our research advances the state of the art in semantic technology, biostatistics, and the modeling of biomedical systems to benefit clinical and translational research as well as patient care. Our work enables the Institute of Medicine’s vision for a Learning Health System by translating biomedical data into actionable insights for decision making.
Our group develops methods to analyze large unstructured data sets for data-driven medicine. We use ontology based approaches to annotate, index and analyze Big Data in biomedicine for enabling data-driven decision making in medicine and health care. We have developed methods that transform unstructured patient notes into a de-identified, temporally ordered, patient-feature matrix. More
The unprecedented wealth of data currently that is being generated in medicine creates new challenges in understanding and using these data for personalized medicine. My lab focuses on exploiting the synergies that are present between data at different scales. More This ranges from molecular data ( e.g., genome sequencing, gene expression), to cellular data (e.g., histological images) and tissue-scale data (e.g., in vivo CT or MR images). We develop computational methods drawn from statistics and mathematics, and apply these to improve decision-support models to personalize diagnosis, prognosis and therapy. Currently the lab focuses on applications in oncology and neuroscience.
Dr. Desai is the Director of the Quantitative Sciences Unit. She is interested in the application of biostatistical methods to all areas of medicine including oncology, nephrology, and endocrinology. She works on methods for the analysis of epidemiologic studies, clinical trials, and studies with missing observations.
Housed in the Stanford Institute for Immunity, Transplantation and Infection, and Biomedical Informatics Research in the Department of Medicine, our lab focuses on novel translational bioinformatics approaches to translation medicine in the broad domains of autoimmunity, infection, and inflammation.