2/5/14 Informatics Research Seminar: Lixia Yao, PhD (presenting from UNC-Charlotte)
Allocation of Research Resources over the Landscape of Medical Conditions in the USA
Discovering and developing innovative and cost-effective medicines that address significant unmet medical needs motivate many governmental and private funding agencies, pharmaceutical and biotech companies, different patient groups, and hundreds of thousands of scientists globally. Given limited resources and time, how to prioritize scientific efforts for the maximal socio-economical benefit becomes a critical question for all of the organizations and individuals above. This is a hard decision making problem because it involves multiple factors in complex and dynamic ways. Previously research labs and pharmaceutical companies rely on estimated disease prevalence and disease burden data from one or a few disease areas to forecast future unmet medical needs, which are either inaccurate or incomplete for critical decision-making and project prioritization. With the emergence of large digitized medical records database and advanced data mining and text mining technologies, it is now possible to investigate unmet medical needs from multiple affecting factors and across the whole disease landscape in a systematic and comparative way. In this project, we analyzed large datasets from a large medical claim database, a scientific literature database, the FDA clinical trial registry and NIH funding distribution over a period of more than 10 years. The goal was to systematically survey the resource allocation situation for the complete disease landscape in the United States during that period, and to identify the ignored niches for future research by investigating the inequalities among disease burden, basic biomedical research, clinical development and public funding.
Dr. Lixia Yao is an assistant professor in the department of software and information systems at UNC Charlotte. Previously, she worked as a scientific investigator at GlaxoSmithKline pharmaceuticals. She received a master’s degree in Computational Sciences from the National University of Singapore in 2004 and a Ph.D. degree in Biomedical Informatics from Columbia University in 2010. Her research and teaching interests are in the broad areas of health informatics. Primarily, her research focuses on applying data mining, text mining and data integration techniques on top of various medical data, including electronic health records, scientific literature and social media data, to address real world problems in healthcare.