A Bayesian Network Model for Predicting Post-stroke Outcomes With Available Risk Factors

Frontiers in Neurology
Eunjeong ParkHyo Suk Nam

Abstract

Bayesian network is an increasingly popular method in modeling uncertain and complex problems, because its interpretability is often more useful than plain prediction. To satisfy the core requirement in medical research to obtain interpretable prediction with high accuracy, we constructed an inference engine for post-stroke outcomes based on Bayesian network classifiers. The prediction system that was trained on data of 3,605 patients with acute stroke forecasts the functional independence at 3 months and the mortality 1 year after stroke. Feature selection methods were applied to eliminate less relevant and redundant features from 76 risk variables. The Bayesian network classifiers were trained with a hill-climbing searching for the qualified network structure and parameters measured by maximum description length. We evaluated and optimized the proposed system to increase the area under the receiver operating characteristic curve (AUC) while ensuring acceptable sensitivity for the class-imbalanced data. The performance evaluation demonstrated that the Bayesian network with selected features by wrapper-type feature selection can predict 3-month functional independence with an AUC of 0.889 using only 19 risk variables and 1-year...Continue Reading

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Citations

Jan 29, 2021·International Journal of Pharmaceutics·Andrea SekulovićJukka Rantanen
May 14, 2021·Journal of Neuroradiology. Journal De Neuroradiologie·Omid ShafaatHouman Sotoudeh

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Clinical Trials Mentioned

NCT02964052

Software Mentioned

SOIS
- Inference System
SOIS Outcome Inference System

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