Juan M. Banda

BioOHDSI Publications

Juan M. Banda, PhD
Assistant Professor of Computer Science
Georgia State University

Dr. Banda is currently an assistant professor of Computer Science at Georgia State University. His lab focuses on building machine learning methods that utilize multi-modal data to generate insights. Previously, Dr. Banda was a research scientist and a postdoctoral data science research fellow at Stanford University in the Stanford Center for Biomedical Informatics Research working in the lab of Dr. Nigam Shah. He has previously held a postdoctoral appointment at Montana State University (2011-2014). He received his Ph.D. degree in Computer Science in 2011 from Montana State University and his M.A degree in Mathematics from Eastern New Mexico University. His main research interests lie in data science, particularly in the big data mining area, and specifically in the knowledge acquisition from massive real-life data sources. Bridging fields between astroinformatics and biomedical informatics, Dr. Banda’s research interests in biomedical informatics involve drug safety and phenotyping, leading the development of Aphrodite, a tool that allows researchers to build electronic phenotypes using fuzzy labels.

Banda JM, Halpern Y, Sontag D, Shah NH. Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network. AMIA Jt Summits Transl Sci Proc. 2017 Jul 26; 2017:48-57. PMID: 28815104.

Duke JD, Ryan PB, Suchard MA, Hripcsak G, Jin P, Reich C, Schwalm MS, Khoma Y, Wu Y, Xu H, Shah NH, Banda JM, J Schuemie M, Risk of angioedema associated with levetiracetam compared with phenytoin: Findings of the observational health data sciences and informatics research network. Epilepsia, Jul 2017. DOI: 10.1111/epi.13828. PMID: 28681416

Hripcsak G, Ryan PB, Duke JD, Shah NH, Park RW, Huser V, Suchard MA, Schuemie MJ, DeFalco FJ, Perotte A, Banda JM, Reich CG, Schilling LM, Matheny ME, Meeker D, Pratt N, Madigan D. Characterizing treatment pathways at scale using the OHDSI network. Proc Natl Acad Sci U S A. 2016 Jul 5;113(27):7329-36. doi: 10.1073/pnas.1510502113. PMID: 27274072.

J. M. Banda, L. Evans, R. S. Vanguri, N. P. Tatonetti, P. B. Ryan & N. H. Shah. “A curated and standardized adverse drug event resource to accelerate drug safety research”. Scientific Data 3, Article number: 160026 (2016) doi:10.1038/sdata.2016.26

J. M. Banda, A. Callahan, R. Winnenburg, H. Strasberg, A. Cami, B. Reis, S. Vilar, G. Hripcsak, M. Dumontier, N.H. Shah. “Feasibility of prioritizing Drug-Drug-Event Associations Found in Electronic Health Records”. Drug Safety Vol. 39(1) pp. 45-57, 2016. DOI: 10.1007/s40264-015-0352-2.

J. M. Banda, T. Kuhn, N.H. Shah, M. Dumontier “Provenance-Centered Dataset of Drug-Drug Interactions”. In Lecture Notes in Computer Science: The Semantic Web – ISWC 2015, Volume 9367, 2015, pp. 293-300, Springer International Publishing, ISBN: 978-3-319-25009-0. DOI: 10.1007/978-3-319-25010-6_18.

V. Agarwal, T. Podchiyska, J. M. Banda, V. Goel, T. I. Leung, E. P. Minty, T. E. Sweeney, E. Gyang, N.H. Shah. “Learning statistical models of phenotypes using noisy labeled training data”. In Journal of the American Medical Informatics Association (JAMIA). DOI: 10.1093/jamia/ocw028.

K. E. Niehaus, J. M. Banda, J. W. Knowles, N. H. Shah. “FIND FH – A phenotype model to identify patients with familial hypercholesterolemia”. In AMIA 2015 Annual Symposium. 2nd Workshop on Data Mining for Medical Informatics: Predictive Analytics. 2015.