We investigate a variety of compelling and novel research problems that are likely to make deep impacts in the future of health-informatics research. Some examples include, how to design sophisticated data mining algorithms to enable predictive analytics on health outcomes (such as, predict the risk of readmission), design decision support framework to optimize certain health outcomes (minimizing readmission risk, mortality, length of stay, or a combination).
We have proposed principled data mining solutions, such as classification algorithms, various generative and discriminative models, as well as statistical analyses and sampling techniques.
The solutions are designed by analyzing a variety of high dimensional large-scale data sources, such as clinical data from EMRs, available nation-wide inpatient datasets, and different types of unstructured data. We have used big data infrastructure, such as Hadoop and Map Reduce to enable large-scale data analytics, as well as, cloud-based solutions.