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Webinar: Diagnostic Decision Support with Bayesian Networks

Healthcare Seminars & Webinars

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.

 

Recorded on February 9, 2018.

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 the 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 the BayesiaLab WebSimulator as a decision support tool for external users.

Webinar Materials

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