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Diagnostic Decision Support with Bayesian Networks

This webinar was recorded on February 9, 2018.

 

In this webinar, we will illustrate how Bayesian networks can serve as a practical tool for optimizing the sequence of diagnostic steps with the objective of arriving at a medical diagnosis in a quick and cost-efficient manner. Bayesian networks allow us to precisely quantify the amount of information contributed from each to-be-observed variable, such as risk factors and symptoms. This capability is one of the key points whereby machine-learned Bayesian networks distinguish themselves from other predictive models, e.g. neural networks.

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

Workflow with the BayesiaLab Software Platform:

  • 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 (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 cost of diagnostic procedures for optimization , i.e. trading off the cost of information gain vs. the expected reduction of uncertainty.
  • Computation of Target Interpretation Tree as a static decision support tool.
  • Publication of the Adaptive Questionnaire to a website as a decision support tool for external users.