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Slide1

Bringing Together the Social and Technical in Big Data Analytics: Why You Can't Predict the Flu from Twitter, and Here's How

David A. BroniatowskiAsst. Prof. EMSEhttp://www.seas.gwu.edu/~broniatowskiSlide2

Public Health Cycle

Population

Doctors

Surveillance

InterventionSlide3

Traditional mechanismsSurveys

Clinical visitsRequires:

Data on the population

This has limited researchSlide4

Twitter

Short messages (140 chars) posted to public internetContent: news, conversation, pointless babbleHuge volume500 million a daySlide5

Why Twitter?

Huge volumes of dataA constant stream of small updatesNothing like waiting in line to buy cigarettes behind a guy in a business suit buying gasoline with ten dollars in dimesI eat pizza too muchI'm at Cvs Pharmacy (117th and kendall, Miami)Slide6

Influenza SurveillanceSlide7

Influenza Surveillance

CDC has nationwide surveillance network with 2700 outpatient centers reportingILI: influenza-like illnessCons:Slow (2 weeks)Varying levels ofgeographicgranularitySlide8

Twitter Surveillance

Twitter influenza surveillance must be1) Accurately track ground truthIdentify infection tweets 2) Effective at both municipal and national levelExpand tweet geolocation and evaluate municipal accuracy

3) Predictive in real time

Deploy previously trained system on this flu seasonSlide9
Slide10
Slide11

Pipeline Classifiers

Three steps using supervised machine learning+NLPStep 1: Identify health tweetsStep 2: Identify flu relatedStep 3: Awareness vs. infectionSlide12

Twitter Surveillance

Twitter influenza surveillance must be1) Accurately track ground truthIdentify infection tweets 2) Effective at both municipal and national levelExpand tweet geolocation and evaluate municipal accuracy

3) Predictive in real time

Deploy previously trained system on this flu seasonSlide13

Local Effectiveness

Current work focuses on US national flu ratesUseful surveillance needed by region/state/cityHow can Twitter track local trends?Is it accurate?Is there enough data?Only about 1% of Twitter is geocodedSlide14
Slide15

Carmen

(Dredze et al., 2013)Over 4000 known locations (countries, states, counties, cities)Geocordinates only: ~1%Expanded locations: ~22%Available in Python and JavaSlide16

Twitter Surveillance

Twitter influenza surveillance must be1) Accurately track ground truthIdentify infection tweets 2) Effective at both municipal and national levelExpand tweet geolocation and evaluate municipal accuracy

3) Predictive in real time

Deploy previously trained system on this flu seasonSlide17

Surveillance Results

Pearson Correlation20092011Keywords

0.97

0.646

Flu Classifier

0.97

0.519

Google Flu Trends

0.97

0.897

Infection

0.972

0.7832Slide18

Google Flu Trends Gets it Wrong?

Lohr, S. (2014). Google flu trends: the limits of big data. New York Times.Slide19

Pearson Correlation:

Keywords: 0.75Infection: 0.93Slide20
Slide21

ILI counts:

Infection: 0.88Keywords: 0.72Blind EvaluationSlide22

2013-2014

0.95 CorrelationSlide23
Slide24

Most recent data

Broniatowski, D. A., Dredze, M., Paul, M. J., & Dugas, A. (2015). Using Social Media to Perform Local Influenza Surveillance in an Inner-City Hospital: A Retrospective Observational Study. JMIR Public Health and Surveillance, 1(1), e5.Slide25

Predicting actual FLU in Baltimore

Broniatowski, D. A., Dredze, M., Paul, M. J., & Dugas, A. (2015). Using Social Media to Perform Local Influenza Surveillance in an Inner-City Hospital: A Retrospective Observational Study. JMIR Public Health and Surveillance, 1(1), e5.Slide26

Healthtweets.orgSlide27

Healthtweets worldwideSlide28

Some Other Projects

David A. BroniatowskiAsst. Prof. EMSEhttp://www.seas.gwu.edu/~broniatowskiSlide29

29

Big

Data for Group Decision Making:

Extracting Social Networks from FDA Advisory Panel Meeting Transcripts

(Broniatowski & Magee,

2013

American Journal of Therapeutics;

Broniatowski & Magee,

2012

IEEE Signal Processing Magazine;

Broniatowski & Magee, in preparation)Slide30

“Germs are Germs” and “Why Not Take A Risk?”Models and Data for Risky Decision Making in the ED

(Broniatowski, Klein, & Reyna

, in press,

Medical Decision Making

Broniatowski & Reyna,

in preparation)Slide31

Examples:

Phylogenetic trees

General Motors

Problem decomposition

Tree Hierarchy

Layered Hierarchy

Examples:

Levels of abstraction

Law firm organization

Problem abstraction

Grid Networks and Teams

Examples:

Contagion

Markets

Crowdsourcing

Families (teams)

How do We Design Systems to Use Information Flow to our advantage?

We would like to

deepen our intuition

regarding system

architectures

(

Broniatowski

& Moses,

in preparation)Slide32

32Questions?

Big dataInfluenza tracking and coupled contagionGroup decision-makingIndividual decision-makingFormal modelsMedical and engineering applicationsFormal and mathematical models Systems architectureDesign for flexibility

broniatowski@

gwu.edu

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Bringing Together the Social and Technical in Big Data Analytics: Why You Can't Predict the Flu fro - Description

David A Broniatowski Asst Prof EMSE http wwwseasgwuedu broniatowski Public Health Cycle Population Doctors Surveillance Intervention Traditional mechanisms Surveys Clinical visits ID: 667352 Download Presentation

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