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Data Mining Applications In Healthcare
TEPR 2004
May 21, 2004
V. Juggy JagannathanVP of Research
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Introduction
Provide an overview of the
technologies that are
relevant to the development
and deployment of datamining solutions in
healthcare
Goals of todays presentation:
Allow participantsto evaluate where
the technology is
useful
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What is
Data mining?
Divining knowledge
from data
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.
Topic Outline
Data mining
Uses
Algorithms
Technology
Applications in
healthcare
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.
Data Mining Uses
Descriptive
Predictive
Classif icat ion
Regression
Time-Series
Cluster ing
Summarizat ion
Assoc iat ion Rules
Sequence Discovery
Understand and characterize
Extrapolate and forecast
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Data Mining Algorithms
Classification
> Statistical
> K-nearestneighbors
> Decision trees
ID3 C4.5
> NeuralNetworks (SelfOrganizingMaps)
Clustering
> Hierarchical
> Partitioned
> Genetic
Association
> Apriori
Algorithm
> If.Then rules
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Technology
Database Technologies
On-Line Analytical Processing
(OLAP)
Visualization Technologies
Data scrubbing technologies
Natural Language Processing
(NLP)
Technology solutions
Data Mining Infrastructure Technologies
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Database Technologies
Data warehouse vs. Data mart
Relational technologies
> Oracle
> Microsoft
XML-databases
> Raining Data
Database
OLAP
Visualization
Scrubbing
NLP
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On-Line Analytical Processing
Analyze multi-dimensional
data
N-dimensional data cubes
Operations
> Roll-up
> Drill-down
> Slice and dice
> Pivot
Database
OLAP
Visualization
Scrubbing
NLP
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Visualization
2D/3D Charts
Topographic displays
Cluster displays
Histograms
Scatter plots
Advanced visualization (genomic data
patterns)
http://www.ncbi.nlm.nih.gov/Tools/
Database
OLAP
Visualization
Scrubbing
NLP
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Data cleansing Filling in missing data
In healthcare, there is a
strong need for de-
identification to protectprivacy
Database
OLAP
Visualization
Scrubbing
NLP
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De-Identification of Medical Records *
Names;
all elements of a street address, city, county,precinct, zip code, & their equivalent
geocodes, except for the initial three digits ofa zip code for areas that contain over 20,000people;
all elements of dates (except year) for dates
directly related to the individual, (e.g., birthdate, admission/discharge dates, date ofdeath); and all ages over 89
and all elements of dates (including year)indicative of such age, except that suchages and elements may be aggregated intoa single category of age 90 or older;
telephone numbers;
fax numbers;
e-mail addresses;
social security numbers;
medical record numbers;
health plan beneficiary numbers;
account numbers;
certificate/license numbers;
license plate numbers, vehicle identifiersand serial numbers;
device identifiers and serial numbers;
URL addresses;
Internet Protocol (IP) address numbers;
biometric identifiers, including finger andvoice prints;
full face photographic images andcomparable images;
any other unique identifying number exceptas created by IHS to re-identify information.
* Source: Policy and Procedures for De-Identification of Protected Health Information and Subsequent Re-Identification 45CFR 164.514(a)-(c) posted by IHS (Indian Health Services)
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Natural Language Processing
NLP Uses
> translation,summarization,informationextraction,
documentretrieval orcategorization
NLP Approaches
> Clustering,
Classification,Linguisticanalysis,knowledge-basedanalysis
NLP Companies inhealth care
> A-Life
> Language andComputing
Database
OLAP
Visualization
Scrubbing
NLP
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Applications in Healthcare
Safety and quality
Clinical Research
Financial
Public Health
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To err is Human IOM Report
Characterization
> JCAHO Core Measures
> CMS Quality measures starter
set
> Improves patient care
reactive response
Prediction
> Identifying cases that can
result in bad clinical outcomes
and raising appropriate alarms
> Impacts patient careproactive response
Safety and Quality
Clinical Research
Financial
Public Health
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Quality Measures Initial Set*
Starter Set of 10 Hospital Quality Measures
Measure Condition
Aspirin at arrivalAcute Myocardial Infarction (AMI)/Heart attack
Aspirin at discharge
Beta-Blocker at arrival
Beta-Blocker at discharge
ACE Inhibitor for left ventricular systolic dysfunction
Left ventricular function assessmentHeart Failure
ACE inhibitor for left ventricular systolic dysfunction
Initial antibiotic timingPneumonia
Pneumococcal vaccination
Oxygenation assessment
*Source: http://www.cms.hhs.gov/quality/hospital/overview.pdf
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Safety and Quality
University of Mississippi Medical Center
> Data Warehouse Technologies to understand
Medication Errors Funded by AHRQ
>Anonymous report data collection> Data mining technologies
> Use of Neural networks and associative rule inference
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Clinical Research & Clinical Trials
Pharmacy and medical
claims data
Drug efficacy and clinical
trials for example howeffective is a particular drug
regimen
Protein structure analysis
Genomic data mining
Diagnostic Imaging data
research
Safety and Quality
Clinical Research
Financial
Public Health
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The bottom line on cost
General Utilization review
does the care provided meet
accepted clinical and cost
guidelines
Drug Utilization review
Outlier analysis exceptions
to treatment analyzing
treatments which cost morethan the normal or less than
normal.
Safety and Quality
Clinical Research
Financial
Public Health
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Data mining in public health
Syndromatic surveillance
Bio-terrorism detection
Communicable disease
reporting (Centers for DiseaseControl (CDC))
DAWN (Drug Awareness and
Warning Network)
Federal Drug Agency (FDA)
reporting of adverse drug
events.
Safety and Quality
Clinical Research
Financial
Public Health
Example effort: AEGIS
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Data mining
Uses
Algorithms
Technology
Applications in
healthcare
Descriptive
Predictive Classification
Clustering
Association rules
Database
OLAP
Visualization
Scrubbing
NLP
Safety and Quality
Clinical Research
Financial
Public Health
Conclusion
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Conclusion
Technology solutions
uestions?
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