Predicting longterm mortality with first week postoperative data after Coronary Artery Bypass Grafting using Machine Learning models
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Proceedings of the 2nd Machine Learning for Healthcare Conference, PMLR 68:3958, 2017.
Abstract
Coronary Artery Bypass Graft (CABG) surgery is the most common cardiac operation and its complications are associated with increased longterm mortality rates. Although many factors are known to be linked to this, much remains to be understood about their exact influence on outcome. In this study we used Machine Learning (ML) algorithms to predict longterm mortality in CABG patients using data from routinely measured clinical parameters from a large cohort of CABG patients (n=5868). We compared the accuracy of 5 different ML models with traditional Cox and Logistic Regression, and report the most important variables in the best performing models. In the validation dataset, the Gradient Boosted Machine (GBM) algorithm was the most accurate (AUROC curve [95%CI] of 0.767 [0.7390.796]), proving to be superior to traditional Cox and logistic regression (p <0.01) for longterm mortality prediction. Measures of variable importance for outcome prediction extracted from the GBM and Random Forest models partly reflected what is known in the literature, but interestingly also highlighted other unexpectedly relevant parameters. In conclusion, we found ML algorithmbased models to be more accurate than traditional Logistic Regression in predicting longterm mortality after CABG. Finally, these models may provide essential input to assist the development of intelligent decision support systems for clinical use.
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