What drew you to MiCHAMP?
I was drawn by its interdisciplinary membership and biweekly seminars. Data science, biostatistics, and machine learning are domain areas with separate perspectives and approaches to addressing a variety of health problems. Through a diverse membership that includes methodologists and clinical experts, the seminars provide a comfortable space for researchers to explore new questions and get opinions from people coming from different viewpoints.
What specific expertise do you bring to MiCHAMP?
I am board certified in internal medicine, nephrology, and clinical informatics. I completed a masters degree in biomedical informatics, in which my masters thesis focused on identification of risk factors for disease progression using natural language processing of clinical notes. In my new role as co-chair of the Michigan Medicine Clinical Intelligence Committee, I think a lot about how to develop and evaluate predictive models as well as how to implement them in clinical settings across the health system
What are your research interests and how do they tie into MiCHAMP?
My research focuses on using informatics and data science to help vulnerable populations. My research spans a number of clinical domains, including emergency care, cancer care, urological and nephrologic conditions, and pharmacoepidemiology. In all of these areas, students in my lab and I are using statistical or machine learning techniques to better understand factors predictive of disease progression, healthcare utilization, or treatment efficacy. Participating in MiCHAMP has really helped me try new approaches to problems that I previously had not even considered.
Gregory B. Auffenberg, Khurshid R. Ghani, Shreyas Ramani, Etiowo Usoro, Brian Denton, Craig Rogers, Benjamin Stockton, David C. Miller, Karandeep Singh, for the Michigan Urological Surgery Improvement Collaborative. askMUSIC: Leveraging a Clinical Registry to Develop a New Machine Learning Model to Inform Patients of Prostate Cancer Treatments Chosen by Similar Men. European Urology. In press.
Elliott Brannon, Tianshi Wang, Jeremy Lapedis, Paul Valenstein, Michael Klinkman, Ellen Bunting, Alice Stanulis, Karandeep Singh. Towards a Learning Health System to Reduce Emergency Department Visits at a Population Level. AMIA Annual Symposium Proceedings. In press.
Singh, K., Drouin, K., Newmark, L. P., Lee, J., Faxvaag, A., Rozenblum, R., . . . Bates, D. W. (2016). Many mobile health apps target high-need, high-cost populations, but gaps remain. Health Affairs, 35(12), 2310-2318. doi:http://dx.doi.org.proxy.lib.umich.edu/10.1377/hlthaff.2016.0578 (PubMed)