Modeling heart procedures from EHRs: An application of exponential families
| Autor: | Shaun J. Grannis, Shuo Yang, Kristian Kersting, Fabian Hadiji, Sriraam Natarajan |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Boosting (machine learning)
Computer science Solid modeling 030204 cardiovascular system & hematology 010501 environmental sciences Poisson distribution 01 natural sciences Electronic mail 03 medical and health sciences symbols.namesake 0302 clinical medicine Exponential family symbols Applied mathematics Probability distribution Graphical model Gradient boosting 0105 earth and related environmental sciences |
| Zdroj: | BIBM |
| DOI: | 10.1109/bibm.2017.8217696 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|