Bio

Education & Certifications


  • Doctor of Philosophy, Montana State University, Computer Science - Data Mining (2011)
  • Master of Arts, Eastern New Mexico University, Mathematics (2007)
  • Bachelor of Science, Universidad Autonoma de Chihuahua, Computer Science (2005)

Publications

All Publications


  • Feasibility of Prioritizing Drug-Drug-Event Associations Found in Electronic Health Records. Drug safety Banda, J. M., Callahan, A., Winnenburg, R., Strasberg, H. R., Cami, A., Reis, B. Y., Vilar, S., Hripcsak, G., Dumontier, M., Shah, N. H. 2016; 39 (1): 45-57

    Abstract

    Several studies have demonstrated the ability to detect adverse events potentially related to multiple drug exposure via data mining. However, the number of putative associations produced by such computational approaches is typically large, making experimental validation difficult. We theorized that those potential associations for which there is evidence from multiple complementary sources are more likely to be true, and explored this idea using a published database of drug-drug-adverse event associations derived from electronic health records (EHRs).We prioritized drug-drug-event associations derived from EHRs using four sources of information: (1) public databases, (2) sources of spontaneous reports, (3) literature, and (4) non-EHR drug-drug interaction (DDI) prediction methods. After pre-filtering the associations by removing those found in public databases, we devised a ranking for associations based on the support from the remaining sources, and evaluated the results of this rank-based prioritization.We collected information for 5983 putative EHR-derived drug-drug-event associations involving 345 drugs and ten adverse events from four data sources and four prediction methods. Only seven drug-drug-event associations (<0.5 %) had support from the majority of evidence sources, and about one third (1777) had support from at least one of the evidence sources.Our proof-of-concept method for scoring putative drug-drug-event associations from EHRs offers a systematic and reproducible way of prioritizing associations for further study. Our findings also quantify the agreement (or lack thereof) among complementary sources of evidence for drug-drug-event associations and highlight the challenges of developing a robust approach for prioritizing signals of these associations.

    View details for DOI 10.1007/s40264-015-0352-2

    View details for PubMedID 26446143

  • Steps Toward a Large-Scale Solar Image Data Analysis to Differentiate Solar Phenomena SOLAR PHYSICS Banda, J. M., Angryk, R. A., Martens, P. C. 2013; 288 (1): 435-462
  • On Dimensionality Reduction for Indexing and Retrieval of Large-Scale Solar Image Data SOLAR PHYSICS Banda, J. M., Angryk, R. A., Martens, P. C. 2013; 283 (1): 113-141
  • A large-scale solar image dataset with labeled event regions The 20th IEEE International Conference on Image Processing (ICIP 2013) Schuh, M. A., Angryk, R. A., Karthik, P. G., Banda, J. M. 2013: 4349 - 4353
  • On the surprisingly accurate transfer of image parameters between medical and solar images. 18th IEEE International Conference on Image Processing (ICIP 2011) Banda, J. M., Angryk, R. A., Martens, P. C. 2011