| Autor: |
Gupta A; Lighthouse Outcomes, Toronto, ON, Canada., Slater JJ; Lighthouse Outcomes, Toronto, ON, Canada., Boyne D; Lighthouse Outcomes, Toronto, ON, Canada.; Cumming School of Medicine, University of Calgary, Calgary, AB, Canada., Mitsakakis N; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada., Béliveau A; Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada., Druzdzel MJ; Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland., Brenner DR; Lighthouse Outcomes, Toronto, ON, Canada.; Cumming School of Medicine, University of Calgary, Calgary, AB, Canada., Hussain S; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada., Arora P; Lighthouse Outcomes, Toronto, ON, Canada.; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada. |
| Abstrakt: |
Objectives. Coronary artery disease (CAD) is the leading cause of death and disease burden worldwide, causing 1 in 7 deaths in the United States alone. Risk prediction models that can learn the complex causal relationships that give rise to CAD from data, instead of merely predicting the risk of disease, have the potential to improve transparency and efficacy of personalized CAD diagnosis and therapy selection for physicians, patients, and other decision makers. Methods. We use Bayesian networks (BNs) to model the risk of CAD using the Z-Alizadehsani data set-a published real-world observational data set of 303 Iranian patients at risk for CAD. We also describe how BNs can be used for incorporation of background knowledge, individual risk prediction, handling missing observations, and adaptive decision making under uncertainty. Results. BNs performed on par with machine-learning classifiers at predicting CAD and showed better probability calibration. They achieved a mean 10-fold area under the receiver-operating characteristic curve (AUC) of 0.93 ± 0.04, which was comparable with the performance of logistic regression with L1 or L2 regularization (AUC: 0.92 ± 0.06), support vector machine (AUC: 0.92 ± 0.06), and artificial neural network (AUC: 0.91 ± 0.05). We describe the use of BNs to predict with missing data and to adaptively calculate prognostic values of individual variables under uncertainty. Conclusion. BNs are powerful and versatile tools for risk prediction and health outcomes research that can complement traditional statistical techniques and are particularly useful in domains in which information is uncertain or incomplete and in which interpretability is important, such as medicine. |
