What do we mean by “AI”?
One way of looking at AI:
Bring people together around a common interest for mutual enrichment.
We are a free meetup open to the public. The key feature we wish members to have is a passion for learning or mastering AI topics.
You can be:
We want to provide resources and systems that allow everyone to flourish!
Let’s take a moment to get acquainted. Please tell us the following:
Introductions after we give them some context.
Key Links & Resources
Brian Griner, PhD
Mention that we hold questions till the end..
Using Bayesian Networks for Segmentation, Targeting & Position of New Products
Bayesian Networks: Nothing to it, right?
The Computer-based Patient Case Study (CPCS) Bayesian Network* *(Pradhan et al. 1994) – 422 nodes: 14 disease, 33 history & risk, 375 health outcomes
Bayesian Network [bey-zee-uhn net-wurk]
1. Informal: A series of interconnected models represented by a specific type of graph (i.e., a Directed Acyclical Graph or DAG)
Example of a Bayesian network with variables as “nodes” and relationships as “arcs”
Behind each node is a conditional probability model linking the node to each of its ‘parent’ nodes
There are multiple relationships being described here.
The distribution of a node (or variable) depends on the distribution of its “parent” nodes (i.e., the nodes that are connected to the target node by arcs)
Let’s pause for a minute and let me show you a very simple example of that relationship.
How a node in a Bayesian network operates
Parent Nodes (X1,X2)
Child Node (Y)
lo : 0.7
med : 0.1
hi : 0.2
Prediction (y | x1,x2)
Make just 3 main points here.
Bayesian Network Model
Series of models,
Dependent, Independent Variables
One dependent variable
Every variable can be
Test relationships between each independent variable and the dependent variable
Test relationship between
Predicts average value of the dependent variable given the levels of each independent variable
Model based on joint probability of
Predicts the relationships of all the variables in a network simultaneously
Predicts the entire joint distribution, not just one dependent variable
Simulating treatment flows and opportunity for
newly launched asthma product
Bayesian network model provided an intuitive graphical model that:
Bayesian Network Case Study
New product launch in crowded Asthma market
Bayesian Network of Asthma Patient Treatment Flow
Select baseline for comparison
Step 1: Select all patients that were NOT candidates for the new therapy
Select target patient type for new therapy
Step 2: Select all patients that were candidates for new therapy
Step 3: Identify patient and physician characteristics of target segments
More characteristics of target segments
Segment profiles provide rich customer portraits of the target physicians and the patients who would be early adopters of the new therapy