Webinar: Diagnostic Decision Support for Coronary Artery Disease

Recorded on February 9, 2018.

Overview

In this webinar, we illustrate how Bayesian networks can serve as a practical tool for optimizing the sequence of diagnostic steps to arrive at a medical diagnosis quickly and cost-effectively. Bayesian networks allow us to precisely quantify the amount of information contributed by each variable to be observed, such as risk factors and symptoms. This capability is one of the key points where Bayesian networks learned from data distinguish themselves from other predictive models, such as neural networks.

We will use the dataset published by Dr. Zahra Alizadeh Sani on Coronary Artery Disease to demonstrate a complete research workflow, from importing the raw data to publishing a final model with a web interface.

Workflow

• Data Import into BayesiaLab.
• Discretization of continuous variables.
• Definition of variable classes.
• Supervised Learning using the Markov Blanket and Augmented Markov Blanket algorithms.
• Structural Coefficient Analysis for Bayesian network model optimization.
• Network Performance Analysis with regard to one or multiple Target Nodes (e.g., Stenosis of LAD, LCX, or RCA).
• Introduction to information-theoretic concepts such as Entropy and Mutual Information.
• 2D Mapping to illustrate Mutual Information between variables and Target Nodes.
• Computation of an interactive and dynamic Adaptive Questionnaire for optimized evidence-seeking with regard to the diagnosis.
• Introduction of the cost of diagnostic procedures for optimization, i.e., trading off the cost of information gain versus the expected reduction of uncertainty.
• Computation of a Target Interpretation Tree as a static decision support tool.
• Publication of the Adaptive Questionnaire to the BayesiaLab WebSimulator as a decision support tool for external users.

Presentation Video

References

• Cardiovascular Imaging Department, Rajaei Cardiovascular, Medical & Research Center, Iran University, Tehran, Iran
  • Updated (Extended) Dataset: https://www.researchgate.net/publication/311582821_extention_of_Z-Alizadeh_sani_dataset 
  • Original (Limited) Dataset: https://archive.ics.uci.edu/ml/datasets/Z-Alizadeh+Sani