Now showing items 1-3 of 3

    • Consensus Statement on Electronic Health Predictive Analytics: A Guiding Framework to Address Challenges 

      Amarasingham, Ruben; Audet, Anne-Marie J.; Bates, David W.; Glenn Cohen, I.; Entwistle, Martin; Escobar, G. J.; Liu, Vincent; Etheredge, Lynn; Lo, Bernard; Ohno-Machado, Lucila; Ram, Sudha; Saria, Suchi; Schilling, Lisa M.; Shahi, Anand; Stewart, Walter F.; Steyerberg, Ewout W.; Xie, Bin (AcademyHealth, 2016)
      Context: The recent explosion in available electronic health record (EHR) data is motivating a rapid expansion of electronic health care predictive analytic (e-HPA) applications, defined as the use of electronic algorithms ...
    • Dynamic Clinical Data Mining: Search Engine-Based Decision Support 

      Celi, Leo Anthony; Zimolzak, Andrew J; Stone, David J (Gunther Eysenbach, 2014)
      The research world is undergoing a transformation into one in which data, on massive levels, is freely shared. In the clinical world, the capture of data on a consistent basis has only recently begun. We propose an operational ...
    • Using MapMyFitness to Place Physical Activity into Neighborhood Context 

      Hirsch, Jana A.; James, Peter; Robinson, Jamaica R. M.; Eastman, Kyler M.; Conley, Kevin D.; Evenson, Kelly R.; Laden, Francine (Frontiers Media S.A., 2014)
      It is difficult to obtain detailed information on the context of physical activity at large geographic scales, such as the entire United States, as well as over long periods of time, such as over years. MapMyFitness is a ...