Data Mining Presentation

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Data Mining Applications In Healthcare

TEPR 2004May 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 data mining solutions in healthcare

Goals of today’s presentation:

Allow participants to evaluate where the technology is useful

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What is Data mining?

Divining knowledgefrom data

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.Topic Outline

Data mining

• Uses

• Algorithms

• Technology

• Applications in healthcare

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.Data Mining Uses

• Descriptive

• Predictive

ClassificationRegressionTime-Series

ClusteringSummarizationAssociation RulesSequence Discovery

Understand and characterize

Extrapolate and forecast

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Data Mining Algorithms

• Classification> Statistical> K-nearest

neighbors> Decision trees

▲ ID3▲ C4.5

> Neural Networks (Self Organizing Maps)

• 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 protect privacy

•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 of a zip code for areas that contain over 20,000 people;

• all elements of dates (except year) for dates directly related to the individual, (e.g., birth date, admission/discharge dates, date of death); and all ages over 89

• and all elements of dates (including year) indicative of such age, except that such ages and elements may be aggregated into a 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 identifiers and serial numbers;

• device identifiers and serial numbers;

• URL addresses;

• Internet Protocol (IP) address numbers;

• biometric identifiers, including finger and voice prints;

• full face photographic images and comparable images;

• any other unique identifying number except as created by IHS to re-identify information.

* Source: Policy and Procedures for De-Identification of Protected Health Information and Subsequent Re-Identification 45 CFR 164.514(a)-(c) posted by IHS (Indian Health Services)

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Natural Language Processing

• NLP Uses> translation,

summarization, information extraction, document retrieval or categorization

• NLP Approaches> Clustering,

Classification, Linguistic analysis, knowledge-based analysis

• NLP Companies in health care> A-Life> Language and

Computing

•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 care – proactive 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 arrival Acute 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 timing Pneumonia

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 how effective 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 more than 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 Disease Control (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]