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PREVENTING SEPSIS: ARTIFICIAL INTELLIGENCE, KNOWLEDGE DISCOVERY, & VISUALIZATION
Phillip Chang, MD (Dept of Surgery) Judy Goldsmith, PhD (Dept of Computer Science)
Remco Chang, PhD (UNC-Charlotte Visualization Center)
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NIH Challenge Grant
This application addresses broad Challenge Area (10) Information Technology for Processing Health Care Data Topic, 10-LM-102*: Advanced decision support for complex clinical decisions
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Clinical Problem: sepsis
Definition: serious medical condition characterized by a whole-body inflammatory state (called a systemic inflammatory response syndrome or SIRS) and the presence of a known or suspected infection
Top 10 causes of death in the US Kills more than 200,000 per year in the
US (more than breast & lung cancer combined)
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Cost of severe sepsis
Estimated cases per year in US: 751,000 Estimated cost per case: $22,100 Estimated total cost per year: $16.7
billion Mortality (in this series): 28% Projected increase 1.5% per annum
Angus et al. Epidemiology of severe sepsis in the United States: Analysis of incidence, outcome, and associated costs of care. Critical Care Medicine. July, 2001
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SIRS
Temperature < 36° C or > 38° C Heart Rate > 90 bpm Respiratory Rate > 20 breaths/min
or PaCO2 < 32 mmHg White Blood Cell Count > 12,000 or <
4,000 cells/mm3; or > 10% bands
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Progression of Disease
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Surviving Sepsis Campaign
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2008 version
Mortality remains 35-60%
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What’s the problem?
Early recognition Biomarkers?
Equivalent of troponin-I for sepsis Alert systems?
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Biomarkers
Not a single marker exist, yet….
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Alert Systems
True alerts Neither sensitive
nor specific Cannot find
“sweet-spot” We’re working on
one now…. Other forms are
“early recognition”
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UK’s “Bob” project
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What about Bob?
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Our premise
Retrospective chart review often yields time frame when one feels early intervention could have changed outcome
Clinical “hunch” that something “bad” might happen which demands more attention
What if we could predict sepsis before sepsis criteria were met?
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Our goal
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How do we do this?
Data Mining Artificial
Intelligence Visualization
(computer-human interface)
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Data! Data! Data!
Temperature
Heartrate
Respiratory Rate
PaCO2
White Blood Cell Count
??????
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Marriage of computer science & medicine
Data mining identify previously undiscovered patterns
and correlations Changes in vital signs Rate of change of the vitals signs Perhaps correlations of seemingly unrelated
events Recently found that prior to significant
hemodynamic compromise, the variation in heart rate actually decreases in mice
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Marriage of computer science & medicine
Decision making Increased monitoring of vitals? More tests? (Which ones?) Antibiotics? Exploratory surgery? None of the above?
What drives decisions? Costs, benefits Likelihood of benefits
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Marriage of computer science & medicine
Artificial Intelligence Model knowledge (from data mining) into
partially observable Markov decision process (POMDP)
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Markov Decision Processes
Actions have probabilistic effects Treatments sometimes work Testing can have effects
The probabilities depend on the patient’s state and the actions
Actions have costs The patient’s state has an immediate
value Quality of life
M = <S, A, Pr, R>, Pr: SxAxS [0,1]
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Decision-Theoretic Planning
“Plans” are policies: Given the patient’s history, the insurance plan (establishes costs) probabilities of effects
Optimize long term expected outcomes
(That’s a lot of possibilities, even for computers!)
(π: S A)
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Partially Observable MDPs
The patient’s state is not fully observable This makes planning harder
Put probabilities on unobserved variables Reason over possible states as well as possible
futures (π: Histories A) Optimality is no longer feasible
Don’t despair! Satisficing policies are possible.
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AI Summary
Use data mining, machine learning to find patterns and predictors
Build POMDP model Find policy that considers long-term
expected costs Get alerts when sepsis is likely,
suggested tests or treatments that are cost- and outcome-effective
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NASA used it….
To reduce “cognitive load”
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Values of Visualization
Presentation
Analysis
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Values of Visualization
Presentation
Analysis
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Values of Visualization
Presentation
Analysis
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Values of Visualization
Presentation
Analysis
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Values of Visualization
Presentation
Analysis
Slide courtesy of Dr. Pat Hanrahan, Stanford
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Values of Visualization
Presentation
Analysis
Slide courtesy of Dr. Pat Hanrahan, Stanford
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Values of Visualization
Presentation
Analysis
Slide courtesy of Dr. Pat Hanrahan, Stanford
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Values of Visualization
Presentation
Analysis
Slide courtesy of Dr. Pat Hanrahan, Stanford
> >
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Values of Visualization
Presentation
Analysis
Slide courtesy of Dr. Pat Hanrahan, Stanford
> >3.14286 3.14084
5
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Values of Visualization
Presentation
Analysis
Slide courtesy of Dr. Pat Hanrahan, Stanford
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Values of Visualization
Presentation
Analysis
Slide courtesy of Dr. Pat Hanrahan, Stanford
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Values of Visualization
Presentation
Analysis
Slide courtesy of Dr. Pat Hanrahan, Stanford
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Values of Visualization
Presentation
Analysis
Slide courtesy of Dr. Pat Hanrahan, Stanford
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Values of Visualization
Presentation
Analysis
Slide courtesy of Dr. Pat Hanrahan, Stanford
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Values of Visualization
Presentation
Analysis
Slide courtesy of Dr. Pat Hanrahan, Stanford
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Values of Visualization
Presentation
Analysis
Slide courtesy of Dr. Pat Hanrahan, Stanford
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Values of Visualization
Presentation
Analysis
Slide courtesy of Dr. Pat Hanrahan, Stanford
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Values of Visualization
Presentation
Analysis
Slide courtesy of Dr. Pat Hanrahan, Stanford
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Values of Visualization
Presentation
Analysis
Slide courtesy of Dr. Pat Hanrahan, Stanford
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Values of Visualization
Presentation
Analysis
Slide courtesy of Dr. Pat Hanrahan, Stanford
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Values of Visualization
Presentation
Analysis
Slide courtesy of Dr. Pat Hanrahan, Stanford
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Values of Visualization
Presentation
Analysis
Slide courtesy of Dr. Pat Hanrahan, Stanford
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Values of Visualization
Presentation
Analysis ?
Slide courtesy of Dr. Pat Hanrahan, Stanford
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Using Visualizations To Solve Real-World Problems…
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Using Visualizations To Solve Real-World Problems…
Where
When
Who
What
Original Data
EvidenceBox
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Using Visualizations To Solve Real-World Problems…
This group’s attacks are not bounded by geo-locations but instead, religious beliefs.
Its attack patterns changed with its developments.
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Visualization concept
It’s your consigliere – always there, in the background
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Visualizing Sepsis
Challenges Connecting to Data Mining and AI
components Doctors don’t sit in front of a computer all
the time…
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Validation
Model will need to be built on retrospective data
Validated on real-time prospective data Clinical trial?
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Leap of faith?