Modeling heart procedures from EHRs: An application of exponential families

Shuo Yang, Fabian Hadiji, Kristian Kersting, Shaun Grannis, Sriraam Natarajan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

    Fingerprint

Keywords

  • EHR
  • clinical events prediction
  • exponential family
  • probabilistic models

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

Cite this

Yang, S., Hadiji, F., Kersting, K., Grannis, S., & Natarajan, S. (2017). Modeling heart procedures from EHRs: An application of exponential families. In I. Yoo, J. H. Zheng, Y. Gong, X. T. Hu, C-R. Shyu, Y. Bromberg, J. Gao, & D. Korkin (Eds.), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 (pp. 491-497). (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2017.8217696