Predicting long-term mortality with first week post-operative data after Coronary Artery Bypass Grafting using Machine Learning models

Jose Castela Forte, Marco A. Wiering, Hjalmar R. Bouma, Fred Geus, Anne H. Epema
Proceedings of the 2nd Machine Learning for Healthcare Conference, PMLR 68:39-58, 2017.

Abstract

Coronary Artery Bypass Graft (CABG) surgery is the most common cardiac operation and its complications are associated with increased long-term 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 long-term 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 [0.739-0.796]), proving to be superior to traditional Cox and logistic regression (p <0.01) for long-term 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 algorithm-based models to be more accurate than traditional Logistic Regression in predicting long-term mortality after CABG. Finally, these models may provide essential input to assist the development of intelligent decision support systems for clinical use.

Cite this Paper

BibTeX
@InProceedings{pmlr-v68-forte17a, title = {Predicting long-term mortality with first week post-operative data after Coronary Artery Bypass Grafting using Machine Learning models}, author = {Forte, Jose Castela and Wiering, Marco A. and Bouma, Hjalmar R. and Geus, Fred and Epema, Anne H.}, booktitle = {Proceedings of the 2nd Machine Learning for Healthcare Conference}, pages = {39--58}, year = {2017}, editor = {Doshi-Velez, Finale and Fackler, Jim and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {68}, series = {Proceedings of Machine Learning Research}, month = {18--19 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v68/forte17a/forte17a.pdf}, url = {https://proceedings.mlr.press/v68/forte17a.html}, abstract = {Coronary Artery Bypass Graft (CABG) surgery is the most common cardiac operation and its complications are associated with increased long-term 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 long-term 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 [0.739-0.796]), proving to be superior to traditional Cox and logistic regression (p <0.01) for long-term 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 algorithm-based models to be more accurate than traditional Logistic Regression in predicting long-term mortality after CABG. Finally, these models may provide essential input to assist the development of intelligent decision support systems for clinical use.} }
Endnote
%0 Conference Paper %T Predicting long-term mortality with first week post-operative data after Coronary Artery Bypass Grafting using Machine Learning models %A Jose Castela Forte %A Marco A. Wiering %A Hjalmar R. Bouma %A Fred Geus %A Anne H. Epema %B Proceedings of the 2nd Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2017 %E Finale Doshi-Velez %E Jim Fackler %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v68-forte17a %I PMLR %P 39--58 %U https://proceedings.mlr.press/v68/forte17a.html %V 68 %X Coronary Artery Bypass Graft (CABG) surgery is the most common cardiac operation and its complications are associated with increased long-term 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 long-term 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 [0.739-0.796]), proving to be superior to traditional Cox and logistic regression (p <0.01) for long-term 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 algorithm-based models to be more accurate than traditional Logistic Regression in predicting long-term mortality after CABG. Finally, these models may provide essential input to assist the development of intelligent decision support systems for clinical use.
APA
Forte, J.C., Wiering, M.A., Bouma, H.R., Geus, F. & Epema, A.H.. (2017). Predicting long-term mortality with first week post-operative data after Coronary Artery Bypass Grafting using Machine Learning models. Proceedings of the 2nd Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 68:39-58 Available from https://proceedings.mlr.press/v68/forte17a.html.

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