2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2017)
Kansas City, MO, USA
Nov. 13, 2017 to Nov. 16, 2017
Shuo Yang , School of Informatics, Computing and Engineering, Indiana University, Bloomington, USA
Fabian Hadiji , TU Dortmund University, Dortmund, Germany
Kristian Kersting , TU Darmstadt University, Darmstadt, Germany
Shaun Grannis , Regenstrief Institute, Bloomington, USA
Sriraam Natarajan , The University of Texas at Dallas, Dallas, USA
In order to facilitate better estimations on coronary artery disease conditions of a patient, we aim to predict the number of Angioplasty (a coronary artery procedure) by taking into account all the information from his/her Electronic Health Record (EHR) data. For this purpose, two exponential family members—multinomial distribution and Poisson distribution models—are considered, which treat the target variable as categorical-valued and count-valued respectively. From the perspective of exponential family, we derive the functional gradient boosting approach for these two distributions and analyze their assumptions with real EHR data. Our empirical results show that Poisson models appear to be more faithful for modeling the number of this procedure.
Probability distribution, Solid modeling, Graphical models, Boosting, Regression tree analysis, Predictive models, Electronic mail
S. Yang, F. Hadiji, K. Kersting, S. Grannis and S. Natarajan, "Modeling heart procedures from EHRs: An application of exponential families," 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA, 2017, pp. 491-497.