Danbury AI

What do we mean by “AI”?

  • AI is an umbrella term for many different subfields which are grouped together by the concept of making programs that behave “intelligently.”

One way of looking at AI:

Meetup Goals

Bring people together around a common interest for mutual enrichment.

  • Share AI information, news, and opportunities.
  • Have monthly talks on interesting AI topics.
  • Develop a team for Kaggle,etc competitions.
  • Create a local hub for AI discussion, research, and collaboration.
  • Create a pool of computational resources for research.

Member Archetypes

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:

  • A student.
  • An expert.
  • A novice.
  • Anyone!

We want to provide resources and systems that allow everyone to flourish!

Introductions

Let’s take a moment to get acquainted. Please tell us the following:

  • Your name
  • Education, Skill Background, or Profession
  • What drew you to the group? What about AI interests you?
  • What would you like to get out of an AI meetup?

Introductions after we give them some context.

Meetup Structure

  • Monthly meetings with interesting talks.
    • We need speakers! Please let us know if you are interested in giving a talk!
    • First Tuesday of every month. Starts at 7pm.
  • Online Collaboration and Connection
    • Slack, Medium Publication, Meetup & Forums, Linkedin Group, Facebook Group.
  • News letter. ( possible )
    • Weekly aggregate of AI news and DanburyAI community communiqués.
  • Suggestions? Message us!

Key Links & Resources

Our speaker:

Brian Griner, PhD

Quick Facts

  • Ph.D., Public Policy Research & Analysis from University of Pittsburgh.

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

A Definition

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”

Physician Characteristics

Patient Characteristics

Age

Severity

Specialty

Current
Rx

Attitude
3

Attitude
1

Attitude
2

Attitude
segment

Symptom
3

Symptom
1

Symptom
2

Disease
Activity

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

X1

X2

0.1

0.3

0.6

1,1

1,0

0,1

0.4

0.2

0.4

0,0

0.5

0.3

0.2

lo

med

hi

Y

1

0

Parent Nodes (X1,X2)

Child Node (Y)

lo : 0.7

med : 0.1

hi : 0.2

Prediction (y | x1,x2)

(x1,x2)

0,1

0.7

0.1

0.2

0.7

0.1

0.2

0.7

0.1

0.2

Make just 3 main points here.


Comparison

Regression Model

Bayesian Network Model

Model Framework

One model

Series of models,
linked as a system

Dependent, Independent Variables

One dependent variable
linked to one or more independent variables

Every variable can be
dependent or independent

Hypothesis Testing

Test relationships between each independent variable and the dependent variable

Test relationship between
each variable and all other variables across the entire network

Prediction

Predicts average value of the dependent variable given the levels of each independent variable

Model based on joint probability of
variables in the network

Predicts the relationships of all the variables in a network simultaneously

Predicts the entire joint distribution, not just one dependent variable

Benefits:

Case Study
Simulating treatment flows and opportunity for
newly launched asthma product

Approach

Bayesian network model provided an intuitive graphical model that:

    • Simulated treatment decisions across physician and patient types
    • Identified targeted patient and physician segmentation
    • Sized the market opportunity
    • Provided promotional material guidance
    • New drug
    • Challenging profile
    • Crowded market
    • Multiple data inputs
      • EMR, Primary Research
        and Publications
    • Weighted segment sizes
      and treatment flows
      to the population

Business Situation

Business Need

    • Treatment flow
    • Stakeholder segmentation
    • Market sizing
    • Promotional strategy

Need:

Approach:

Results:

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

In Conclusion

      • Bayesian Networks provide a simple way to link individual models into a system of cause and effect relationships
      • BNs can predict the impact of a change in one or more variables on ALL variables across the network
      • BNs are useful tools for simulating market dynamics (e.g., buying process) and identifying joint physician and patient segments for targeted promotions

DanburyAI-Introduction & Presentation - Google Slides