Webinar Recording

Diagnostic Decision Support with Bayesian Networks

Recorded on February 9, 2018.

Webinar Materials

• Presentation Slides (PDF, 29.5 MB)
• BayesiaLab Network File (XBL, 26 KB)
• Dataset (CSV, 55 KB)
• Node Comments (TXT, 1 KB)
• Node Classes (TXT, 2 KB)

Abstract

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 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.