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

No clue what use this could be. Survivors want to know the Bayesian predictions of protocol results. 
A Bayesian Network Model for Predicting Post-stroke Outcomes With Available Risk Factors 

Eunjeong Park¹, Hyuk-jae Chang² and Hyo Suk Nam^3*

• ¹Cardiovascular Research Institute, College of Medicine, Yonsei University, Seoul, South Korea
• ²Department of Cardiology, College of Medicine, Yonsei University, Seoul, South Korea
• ³Department of Neurology, College of Medicine, Yonsei University, Seoul, South Korea

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 mortality with an AUC of 0.893 using 24 variables. The Bayesian network with 50 features filtered by information gain can predict 3-month functional independence with an AUC of 0.875 and 1-year mortality with an AUC of 0.895. We also built an online prediction service, Yonsei Stroke Outcome Inference System, to substantialize the proposed solution for patients with stroke.

Introduction

A stroke is the second most common cause of death in the world and a leading cause of long-term disability. Patients with stroke have higher mortality than age- and sex-matched subjects who have not experienced a stroke. It is also reported that strokes recur in 6–20% of patients, and approximately two-thirds of stroke survivors continue to have functional deficits that are associated with diminished quality of life (1). Such disability after stroke can be measured by the modified Rankin scale that categorizes functional ability from 0 to 6 (2–4). To discriminate the effect of clinical treatment for patients with ischemic stroke, a score on the modified Rankin scale 0–2 is widely applied for the indication of functional independence after stroke (2).

There are many prognostic models for the functional outcomes and risk of death after stroke. However, an agreed set of guidelines or reporting for the development of prognostic score models are currently unavailable. In a recent systematic review of clinical prediction models, the discriminative performances of models were still unsatisfactory, with the AUC values ranging from 0.60 to 0.72, which are similar to the predictability of experienced clinicians (5).

The prediction of prognosis needs to employ a variety of statistical, probabilistic, and optimization techniques to learn patterns from large, complex, and unbalanced medical data. This complexity challenges researchers to apply machine learning techniques to diagnose and predict the progress of the disease (6, 7). Machine learning has been expected to dramatically improve prognosis, and certain applications have achieved remarkable results (7). These applications have employed various machine learning techniques including a deep neural network (8), support vector machine (8, 9), decision trees (10), and ensemble methods (11, 12) to classify diseases, level of deficits, and morality. Selecting the optimal solution for a decision problem should consider the unique pattern of a data set and the specific characteristics of the problem (13).

The Bayesian network, a machine learning method, predicts and describes classification based on the Bayes theorem (14). Bayesian networks are widely used in medical decision support for their ability to intuitively encapsulate cause and effect relationships between factors that are stored in medical data (15, 16). With these characteristics of conditional probabilities, the Bayesian network can provide interpretable classifiers by logic inherent in a decision support (17, 18). The parameters and their dependences with conditional probabilities of the Bayesian network can be provided either by experts' knowledge (16, 19) or by automatic learning from data (20, 21). In addition, Bayesian networks can be used to query any given node in the network and are therefore substantially more useful in clinics compared with classifiers built based on specific outcome variables (22).

In this study, our aim was to investigate the usefulness of a machine learning method to forecast functional recovery for independent activities and 1-year mortality in patients with acute ischemic stroke. We also introduced an online inference system for predicting functional independence at 3 months and mortality in 1 year of patients with stroke based on the proposed Bayesian network.