Healthcare costs are a major driving factor of the national debt, while at the same time, misdiagnoses cost the U.S. 800,000 deaths and serious disabilities every year. AI technologies can be leveraged to simultaneously improve the quality of healthcare and reduce the healthcare cost. In addition, the best way to reduce healthcare costs is to improve the overall wellbeing of the population, thus NIH has recently published its Strategic Plan for NIH Nutrition Research, where AI technologies will also play an important role.

We are broadly interested in applying DataScope to the healthcare domain and developing intelligent task agents for use by patients and/or physicians to augment their intelligence and optimize decision making, thus delivering broad benefits to our society.

Some of our work in this direction can be found in the following sources:

• Dissertation of Eddie Huang titled "Integrating heterogeneous data into electronic medical record analysis">, which covers multiple papers on analysis of electronic medical records (EMRs).
• Medical DataScope Talk which briefly covers multiple lines of our work.
• Talk at Univ. of Pittsburgh on Statistical Approaches to Analysis of Traditional Chinese Medicine Patient Records.