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Webinar Recording

Diagnostic Decision Support with Bayesian Networks

Recorded on February 9, 2018.

 

Webinar Materials

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.

BayesiaLab Courses

May 8–10, 2019 Singapore Introductory Course (3 Days)
May 13–15, 2019 Sydney, Australia Introductory Course (3 Days)
May 21–23, 2019 Paris, France Advanced Course (3 Days, in French) 
June 5, 2019 Washington, D.C. BayesiaLab 101 Short Course (1 Day)
June 12–14, 2019 Seattle, WA Introductory Course (3 Days)
June 17–19, 2019 Seattle, WA Advanced Course (3 Days)

Upcoming Seminars, Webinars, and Conferences

Live Webinar May 16, 2019 11:00 – 12:00 (CDT, UTC-5) Human-Machine Teaming
Live Webinar May 30, 2019 11:00 – 12:00 (CDT, UTC-5) Causal Counterfactuals for Contribution Analysis — Explaining a Misunderstood Concept with Bayesian Networks
Live Webinar June 13, 2019 11:00 – 12:00 (CDT, UTC-5) Black Swans & Bayesian Networks — Jointly Representing Common and Rare Events
Please check out our archive of recordings of previous events.

7th Annual BayesiaLab Conference

October 7–9, 2019 Durham, NC 3-Day Introductory Course
October 10–11, 2019 Durham, NC 7th Annual BayesiaLab Conference
October 14–16, 2019 Durham, NC 3-Day Advanced Course