Improving Predictive Models with
Machine Learning & Big Data
2015 NJ/DV HIMSS Fall Conference
© SpectraMedix, 2015. The contents of this presentation are confidential and cannot be copied without prior written permission from SpectraMedix.
1
Learning Objectives
1.
Predictive Modeling in Healthcare - Why Predict?
2.
Use Cases: Existing Predictive Modeling Techniques
a.
Reducing Preventable Readmissions
b.
Population Health Management
3.
Improving Healthcare Predictive Models with Machine Learning
and Big Data
4.
Integrating Machine Learning and Big Data into Predictive Models
5.
Use Cases: Enhanced Predictive Modeling
a.
Reducing Preventable Readmissions
b.
Population Health Management
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Predictive Modeling: Why Predict?
Predictive Modeling helps institutions anticipate risks and better
prepare, organize and align resources to tackle those risks.
Predict
Predict future
risk for events
of interest
Measure
Measure
effectiveness
of prediction
and intervention
Plan
Plan on how to act
to intervene
Perform
Deploy plan
to intervene
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3
Predictive Models in Healthcare
Predictive models in healthcare extract useful insights from
the industry’s rich and expanded data sources in real time.
- Optimizations predictive analytics
- Complex statistical analysis
- Machine learning
- All types of data, and many sources
- Very large datasets
- More real-time
- Ad-hoc querying and reporting
- Data mining techniques
- Structured data, typical sources
- Small to mid-size datasets
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Use Case 1: Existing Predictive Modeling
Techniques to Reducing Preventable Readmissions
Readmissions in numbers
In 2011 there were 3.3 million
hospital readmissions
They contributed $41.3 billion
in total hospital costs
Medicare had the largest share of
total readmissions (55.9%) and
associated costs (58.2%) followed
by Medicaid (20.6 %)
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Use Case 1: Existing Predictive Modeling
Techniques to Reducing Preventable Readmissions
LACE
1. How it works
•
L=Length of Stay (LOS)
•
A= Acuity
•
C= Co-morbidities based on the “Charlson Comorbidity Index”
•
E= Previous emergency room visits
2. Challenges
a.
Info from the LACE model is generally delivered too late to have
an impact – when patient is already moving to discharge
b.
LACE has moderate predictive power
c.
LACE alone does not provide actionable insights
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Use Case 2: Existing Predictive Modeling Techniques
for Population Health Management (ESRD)
ESRD (End-stage renal disease) in numbers
• 26M+ Americans have kidney disease:
precursor to ESRD
• 1 in 3 American adults at risk to develop
kidney disease
• ~450k Americans are on dialysis
Annual medical payments for a kidney
disease patient increases from $15k in
Stage 3 to $70k+ in Stage 5
In 2012, Medicare expenditures for all
stages of kidney disease was $87B+.
~$58B was spent caring for chronic
kidney disease
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Use Case 2: Existing Predictive Modeling Techniques
for Population Health Management (ESRD)
Current ESRD prediction models take into account various
demographic and clinical factors.
Age
High BP
Race
ESRD
Gender
CHF
Diabetes
Digital healthcare devices have made regular health monitoring possible
which opens up a wealth of information to make better predictions to
prevent or plan for events.
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Use Case 2: Existing Predictive Modeling Techniques
for Population Health Management (ESRD)
Challenges
•
Models rely on claims data
•
Models take into account limited risk factors
•
Problem of dealing with large numbers of potential predictors:
○ >90,000 ICD-10 diagnostic codes, >4,000 procedures,
>7,000 medications
•
Managing the Utility-Privacy tradeoff: Inability to join healthcare data
with other sources of critical information to ensure patient privacy
•
Moderate predictive power
•
Limited actionable insights
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9
Data Explosion in Healthcare
Progress and innovation are no longer hindered by the ability
to collect data
EHR Data
Claims
Data
Diagnostics
Predictive
Models
Wearable
Devices
Mobile Apps
Social Media
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10
Improving Healthcare Predictive Models
with Machine Learning and Big Data
• The next generation of predictive models incorporate real
time data, text mining, machine learning and big data
• Use real-time data to make results relevant and timely
• They make predictive models actionable
• Improve accuracy - prescriptive and customizable analytics
based on the needs of the hospital
• More generalizable across patient sub populations
• Easier to implement – machine learning makes them automated
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Understanding Big Data
Big Data: Data sets so large or complex that
traditional data processing applications are inadequate.
Key Themes of Big Data
Data explosion
Need to access data and store it
Structured-unstructured data
Need for big data architecture to harness it
“Data Lakes” and other big data concepts
Big data tools – Hadoop and MapReduce
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Four Dimensions of Big Data
Big data is characterized by 4Vs that set it apart from traditional data
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Big Data Technical Architecture
The Emerging Big Data Stack
Insights
Analytics
Storage
Determines what questions need data
based answers and how the outputs need
to be presented
Enables execution of complex algorithms
across nodes and then aggregates - where
predictive modeling and machine
algorithms are executed
Enables faster, scalable storage and
retrieval by harnessing processing and
storage capabilities of multiple nodes
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Machine Learning to the Rescue!
Increased computing power and
advancement in computer science have
resulted in the development of
sophisticated machine learning algorithms
that enable intelligent mining of the big
data.
Machine Learning uses Big Data
to Improve the Performance of
Predictive Models
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Why Machine Learning?
Machine Learning enables us to generate meaningful
insights from Big Data to drive business more effectively
•
Relationships and correlations hidden within large amounts of
data can be discovered using Machine Learning
•
Amount of knowledge available about certain tasks might be too
large for explicit encoding by humans
•
New knowledge about tasks is constantly being discovered. It is
inefficient and difficult to continuously re-design systems “by hand”
•
Environments change over time
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What is Machine Learning
Machine learning explores the study and construction
of algorithms that can learn from and make predictions
on data – Wikipedia
=
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Machine Learning Structure
Source: http://xiaochongzhang.me/blog/wpcontent/uploads/2013/05/MapReduce_Work_Structure.png
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Use Case 1A:
Enhanced Predictive Modeling to Identify
Patients at High Risk for Readmissions
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19
Predictive model algorithms with sophisticated ML techniques
and a wide variety of predictors will exhibit better accuracy
Predictors in LACE
Additional Predictors
in our model
Other Potential
Predictors
Discharged to,
Admit Source
Length Of
Stay
Other Socio
Economic factors
Financial
Class
Acute vs.
Predicting
Readmissions
Emergent
Medication
details
# of Visits
Co-
Patient Age
morbidities
Other
Behavioral
factors
ED Visits in
past 6
months
Primary
Condition
• Additional variables are important predictors of readmission
• Sophisticated machine learning techniques like SVM enhanced predictive accuracy of the algorithm
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Predictive Model: Readmission Risk Prediction
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Use Case 2a:
Enhanced Predictive Modeling to
Predict ESRD in Elderly Patients
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22
Current ESRD Predictive Models
CMS hierarchical condition categories (CMS-HCC) model
Age
High BP
Race
ESRD
Gender
CHF
Diabetes
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23
How we Improved the Existing Models
Scope
Identify patients at high risk of developing
chronic kidney disease
Identify patients at high risk of
transitioning from chronic kidney disease
to end stage renal disease
Enable wider data collection by tapping into
unconventional sources like labs, radiology and
personal data
Use big data architecture to store data in data
lakes and use on need basis
Utilize machine learning techniques to do best
possible predictions with available data on case to
case basis
Challenges
Extensive data collection,
storage and processing
Developing trend analysis based on
incomplete data
Incorporating unstructured data into
predictive models that utilize cutting edge
machine learning techniques
Enhanced predictive power (Higher
True Positives, Lower False Positives)
Key indicators increasing the patient
risk for patient specific intervention
strategies
Solution
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Benefits
24
Population Health Risk Assessment Modeling
Analysis of data using various statistical and machine learning
techniques helped identify patients at high risk of ESRD progression
Need of intervention
Low
Medium
High
Very high
Patient Data Analysis
Modeling is performed using patient level data
Patient
Demographics
Medical History
Behavioral
Factors
Genetic Factors
Machine
Learning Tech.
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Statistical
Modeling
25
Questions?
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Presenter Contact Information
Raj Lakhanpal, MD
CEO, SpectraMedix
609-336-7733, x301 (office)
609-865-3244 (cell)
Raj.Lakhanpal@SpectraMedixcom
Indranil Ganguly
Vice President & CIO
JFK Health
732-321-7702
IGanguly@JFKHealth.org
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