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Modeling heart procedures from EHRs: An application of exponential families

Shuo Yang; Fabian Hadiji; Kristian Kersting; Shaun J. Grannis; Sriraam Natarajan
In: Xiaohua Hu; Chi-Ren Shyu; Yana Bromberg; Jean Gao; Yang Gong; Dmitry Korkin; Illhoi Yoo; Huiru Jane Zheng (Hrsg.). 2017 IEEE International Conference on Bioinformatics and Biomedicine. IEEE International Conference on Bioinformatics and Biomedicine (BIBM-2017), November 13-16, Kansas City, MO, USA, Pages 491-497, IEEE Computer Society, 2017.


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.

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