<img height="1" width="1" style="display:none" src="https://www.facebook.com/tr?id=648880075207035&amp;ev=PageView&amp;noscript=1">

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

December 10–12, 2019 New York, NY, USA 3-Day Introductory Course
February 5–7, 2020 Singapore 3-Day Introductory Course
February 10–12, 2020 Sydney, NSW, Australia 3-Day Introductory Course
March 3–5, 2020 Dubai, UAE 3-Day Introductory Course
March 9–11, 2020 Dubai, UAE 3-Day Advanced Course
March 24–26, 2020 Boston, MA, USA 3-Day Introductory Course
April 7–9, 2020 Paris, France 3-Day Introductory Course
May 6–8, 2020 Seattle, WA, USA 3-Day Introductory Course
May 11–13, 2020 Seattle, WA, USA 3-Day Advanced Course
June 15–17, 2020 Paris, France 3-Day Advanced Course
October 5–7, 2020 Toronto, ON, Canada 3-Day Introductory Course
October 13–15, 2020 Toronto, ON, Canada 3-Day Advanced Course

Seminars, Webinars, and Conferences

December 12, 2019
2 p.m. – 5 p.m. (EST, UTC-05)
Free Seminar in New York, NY Artificial Intelligence for Judicial Reasoning
January 21, 2020
2 p.m. – 5 p.m. (EST, UTC-05)
Free Seminar in Washington, DC Artificial Intelligence for Judicial Reasoning
January 28, 2020, 11 a.m. – 12 p.m. (CST, UTC-06) Live Webinar Bayesian Parameter Estimation for Individualized Drug Dosing
January 30, 2020
2 p.m. – 5 p.m. (CST, UTC-06)
Free Seminar in Chicago, IL Artificial Intelligence for Judicial Reasoning
Please check out our archive of recordings of previous events

8th Annual BayesiaLab Conference

October 5–7, 2020 Toronto, ON, Canada 3-Day Introductory Course
October 8–9, 2020 Toronto, ON, Canada 8th Annual BayesiaLab Conference
October 13–15, 2020 Toronto, ON, Canada 3-Day Advanced Course