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Population Health Sub-text comes here

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Predictive Analytics Machine Learning

To Advance Population Health

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Discussion Host

Dr. George Wu,Vice President Edifecs, Inc.

Ankur Teredesai, PhD, Professor & Director, University of WA Center for Data Science

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1 Introduction

2 Analytics: Past and Present

3 Data Tsunami Challenges

4 Analytics & Machine Learning Applied to Healthcare

5 Q&A

Agenda

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Analytics

Future A

Future B

Population

You

What’s the risk?

Which risks?

How to prevent?(prescriptive)

No hypothesis required

(i.e., no prior medical

knowledge)

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Analytics Amazon

• Puts items in cart prior to order• Greater customer satisfaction (faster

delivery)• Supply chain and logistics optimizationU.S. Department of VA and Dartmouth School of Medicine1

• Durkheim Project• Opt-in database of 100,000 veterans

and millions of social media posts• Identify risk factors in military suicides

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Data Tsunami

Healthcare data is increasing in volume by 48% per

year

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Center for Data Science: Societal Impact

• Bioinformatics• Health and

Wellness• Predictive Analytics

Health Informatics

• Distributed Systems

• Databases• Geo-Spatial• Embedded

SystemsGeo-Spatial Data Management

• Machine Learning• Data Mining• Computation

Intelligence• Computer Vision

Intelligent Systems

• Web• Devices• Mobile Networks• UX / UI

Social Computing

• Cryptology• Secure Machine

Learning

Big Data Security

• Engineering• Dev-Ops

Big Data Infrastructure

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Predictive Analytics: Why and When?

Common Question: Machine Learning instead of Statistical Regression

The goal is to predict accurately vs. simple explaining a phenomenon

Predictor variables• Too many factors

(25+)• Risk of

Readmission (Demographics, Labs, Vitals, Claims, Comorbidity, LOS)

• Highly Correlated

The predictors have non-linear relationships to the target variable• Linear: Childrens

Medication::Weight

• Non-Linear: Effect of medication over time useful then harmful beyond a time

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iTORNADO

iTornado

Routing Service With Real World Severe Weather

Demo Paper in ACM SIGSPATIAL 2014(Best Demo paper award)

Fatalities Stats by Weather Related Hazards http://www.nws.noaa.gov, June 2014.

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Our Focus: Predictive Population Health Applications

Chronic Condition Care Management

Scalable ACO

Population/Individual Cost Analysis

Bioinformatics and Systems Biology

Readmission as a Service

Scalable Cost Prediction

Personalized Medicine

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Accountable Care OrganizationsCost/Charge Prediction

HealthSCOPE: An Interactive Distributed Data Mining Framework for Scalable Prediction of Healthcare Costs

Marquardt James, Newman Stacey, Hattarki Deepa, Srinivasan Rajagopalan, Sushmita Shanu, Ram Prabhu, Prasad Viren, Hazel David, Ramesh Archana, De Cock Martine, Teredesai Ankur, IEEE Data Mining Conference Demo Track, 2014 IEEE International Conference on DOI: 10.1109/ICDMW.2014.45 Publication Year: 2014 , Page(s): 1227 - 1230

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Motivation: ACO Cost Prediction

Demographics

Diagnosis Codes

Procedure Codes

DrugsLab ResultsC

linic

al

Cla

ims

Sources : SID, OSHPD, MEPS Source : MultiCare Collaboration

ChargesVitals

Health Prediction

Care Management

What are healthcare costs for assigned

population in 2015 ?

Why is the cost so high or low ?

How does the cost distribute across demographics ?

Population Predictive Modeling

Feature Prioritizatio

n

Individual Predictive Modeling

Chandola et. al, KDD 2013

QU

ESTI

ON

SD

ATA

SCIE

NCE

DAT

A

APPLICATIONS

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Cost/Charge Prediction Problem

Predict future healthcare charges of individualsBased on past medical history and charges information

Goal:

Supervised Machine Learning

Technique:Previous health information (e.g. diagnosis, comorbidities, etc). General demographics (age, gender, race)Previous healthcare cost{X} = (x1, x2, x3 ......xp)

Input X:Future healthcare cost in $

Output $Y:

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Predictive Cost Analytics: 4 Scenarios

3 Months of Historical data (Medical, Demographic and Cost)• Cost of Following Nine months (1Q)

6 Months of Historical data (Medical, Demographic and Cost)• Cost of Following Six months (2Q)

9 Months of Historical data (Medical, Demographic and Cost)• Cost of Following Three months (3Q)

12 Months of Historical data (Medical, Demographic and Cost)• Cost of Following Twelve months (4Q)

Access the webinar in full Recorded webinar

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Next Steps

Other Resources

ContactShareApplying Data for Population Health Recorded webinar Questions?

alison.south@edifecs.com