Download Slide 1

Artificial Intelligence in Healthcare
Casey C. Bennett
1Dept.
of Informatics
Centerstone Research Institute
Nashville, TN, USA
2School
of Informatics and Computing
Indiana University
Bloomington, IN, USA
Cognitive Offload
We can make predictions and discover
patterns, but what do you do with them?
Building a Pipeline
Data
Patterns
Predictions
Decisions
Step 3
1
2
Make Decisions
Predictions
Clinical Indicators
Socio-demographic
Genetic Data
Etc.
aPatterns
= Action/Treatment
s = Patient State
Patterns
o = Observation
c = Costs
t = Time
Predictions
cpuc = Utility
Transition
Models
Markov Decision Processes (MDPs)
Machine Learning/Statistical Techniques – predict risk stratification,
treatment response ,Dynamic
survival, re-hospitalization,
LOS, etc.
Decision Networks
(DDNs)
Step 3
Make Decisions
a = Action/Treatment
s = Patient State
o = Observation
c = Costs
t = Time
cpuc = Utility
Belief States
Plan over Time
Plan/re-plan
Bennett CC and K Hauser (2013) “Artificial Intelligence Framework for Simulating Clinical Decision-Making: A
Markov Decision Process Approach.” Artificial Intelligence in Medicine. 57(1): 9-19.
Bennett CC and K Hauser (2013) “Artificial Intelligence Framework for Simulating Clinical Decision-Making: A
Markov Decision Process Approach.” Artificial Intelligence in Medicine. 57(1): 9-19.
Building a Pipeline
Data
Patterns
Predictions
Decisions
Simplification
1
2
Components to
classify/cluster/etc.
Find Patterns
Components that
determine optimal
actions, based on
those patterns
Make Decisions
Relativity
Natural Selection
Darwin
Einstein
Cognitive Computing
IBM and others
Botvinick M (2012) “Heirarchical Relational Learning and Decision Making.” Current Opinions in Neurobiology.
22(6): 956-962.
The Future?
Personalized AI?
Ongoing Collaborations
Thank you
-
www.CaseyBennett.com
-
http://r-house.soic.indiana.edu/ - IU Human-Robot Interaction Lab
-
www.CenterstoneResearch.org - CRI