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  • 7/27/2019 Data Mining in Heathcare

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    Data Mining Applications In Healthcare

    TEPR 2004

    May 21, 2004

    V. Juggy JagannathanVP of Research

    [email protected]

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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

    http://thumbpicked%2811%29/http://thumbpicked%2811%29/http://www.ncbi.nlm.nih.gov/Tools/http://www.ncbi.nlm.nih.gov/Tools/http://thumbpicked%2811%29/

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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?

    [email protected]

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