paper #63144

Oracle Data Mining and Epidemiological Analysis

Scott A. Rappoport, OCP

MTS Technologies

OracleWorld 2003

San Francisco, CA

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Presentation Goals

Short intros Vocabulary

Present Basic Medical Terms Describe Data Mining Models and Terms

Synthesize What questions are we asking? Applying DM to Epidemiological issues

Demonstrate the DM4J components The future:

Challenges 10g features

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The DM Dimension

Data Mining capability readily accessible to the end users opens a whole new dimension of what can be performed in the medicine.

New questions are being generated based on the availability of these new techniques.

This is a cutting edge (bleeding edge) advanced technique

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A few disclaimers…

Medical data is highly sensitive information… Thus:

No personally identifiable info is presented No specific aggregated information on disease types,

locations, or time is provided Scaled back list of attributes in demos

However, demos will give an indicative application of the technology.

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About you

What percentage of the audience: Has a medical background?

Physician Epidemiology/research/academic

Has an IT background? DBA Developer

Knows a lot about Data Mining? Statistics?

Has at least two of the above? Three?

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About me

Oracle Certified DBA and Developer ASQ Certified Quality Engineer Principal Architect, supporting the Naval Health

Research Center in San Diego, CA Instructor of Oracle, Data Warehouse, and Web

Services courses at UCSD-Extension Papers on Java, DataWarehousing – IOUG/ODTUG Biochemistry degree/ worked in a diagnostics firm Son of a clinical pathologist

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Let’s get at it…

The Medical Side

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Medical Lexicon Epidemiology

Study of the relationships of various factors determining the frequency and outbreak of disease.

Nosocomial Outbreaks originating within a hospital.

Nosology Study of the classification of diseases.

ICD9/10 International Classification of Diseases: v9 or 10. Classification of

disease by major category – represented by a three-digit code, followed by a specific type, represented by a two-digit code.

DNBI: Disease Non-Battle Injury. Military classification of disease types.

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Nosology/ICD9 Disease Classification

Over 12,000 separate diseases Classified into 13 areas Further sub-classed Set off by 3 digit code, then additional 2 digit

descriptor for better granularity DNBI – military designations

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Epidemiological/Medical Practice Questions

What factors affect the onset of disease within a population?

What is the likelihood that a patient will require follow-up treatment, hospitalization, or that the case will worsen?

Are there particular clusters of patients that are more likely to develop a certain disease?

How often is a case mis-diagnosed? Is a particular treatment likely to cure the ailment?

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Summarizing the Concerns

Predictive concerns Classification of risks and subjects Attribute ranking concerns Multi-factor relevance Dealing with large numbers of attributes Clustering questions Unknown associations

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Epidemiological techniques

Statistical packages Chi-square ANOVA / ANCOVA / MANOVA Multi-variate Analysis (Attribute Scoring):

Multiple Logistic Regression (binomial/dichotomous)Multiple Linear Regression (multiple/category)

Covariance 2x2 matrix

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Risk factors/classification

Environmental: exposure, location, job risks, diet Genetic: Genetic markers present? Clinical: Blood/other diagnostics data Familial: Other family members? Who, what? History: Past illnesses? What? When? How often? Socio-economic: Job, married, education, age, gender Lifestyle: Exercise, smoker, alcohol Ethnic/National/Geographical

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Patient Data Universe

Patient history

Diagnosticdata

Family History

Treatment Fac/Personnel

Geographicfactors

Lifestylefactors

Druginteractions

Genomic

Total Patient DescriptionPhysician'snote

Ethnic/race/national

A vast amount of data potentially to be collected andmined in the patient data universe !!!

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The Data Mining Side

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Reporting techniques/hierarchies

Data

DataMining

On-LineAnalytical

Processing(OLAP)

Ad HocQueries

Operational ReportingWhat (specific events)happened yesterday?

What hiddenassociations or

clusters of attributesmay exist?

What is likely tohappen tomorrow

(based on past trends/aggregations)?

Why did that happenyesterday?

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Reporting Examples

Query Technique

Reporting needs Example

Operational reporting

Basic information on an event Find the diagnosis of patient #A1234 on this date.

Ad-hoc User define queries to help understand an event

Does the specific patient have a past history of such a diagnosis?

OLAP Summarized data of events across many dimensions

What is the incidence rate of this disease among this patient type? For this area, season, hospital, etc? Is this becoming more prevalent?Data Mining Attribute associations,

predictive modeling, clustering of populations by attribute sets.Across many attributes and records

What are the risk factors for this disease? What is the likelihood a treatment will succeed for a patient? What specific populations are at risk?

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Data Mining Techniques

Classification Seeks to find out attributes that best predict a dependent variable

Clustering Seeks groupings of attributes in populations

Association What is the likelihood that event A will lead to or occur with event B,

C, or D… Attribute Importance

Ranking of attributes based on their effects on a given dependent variable

Lift Model: Measures how well a model can identify a given target

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Data Mining Terms

Confusion Matrix: Tests model accuracy. Actual to predicted evaluated, scored by

incidence of false-positives / false-negatives. False-negative:

disease present, results not shown False-positive:

disease not present, results show Supervised learning:

target value is specified. Classification / regression Unsupervised learning:

Relations/target attributes not known. Clusters/Assoc

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Data Mining Terms (cont’d)

Support: The measure of how often the collection of items in an

association occur together as a percentage of all the transactions.

Confidence: Confidence of rule "B given A" is a measure of how much

more likely it is that B occurs when A has occurred. ROC:

Receiver Operating Characteristic. Used in Lift models to determine how well the model identifies targets as opposed to random selection.

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Supervised/Unsupervised

Supervised Prediction odds of success Classification

ModelTest (obtain false-positives/negativesApplyLift

Attribute ImportanceDetermine attributes with the most effect on resultWant to split on this attribute

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Supervised/Unsupervised

Unsupervised No a priori knowledge find hidden relations/ associations/ groupings Clustering

What groups of subjects share values of attributes that are closely related?

AssociationsFind events that are related; i.e., if A (and/or B)

happens, what are the odds that C will happen?

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Classification Modeling

Used to find a predictive model of independent attributes on the outcome of a dependent attribute

Algorithms: Naïve Bayes, Adaptive Bayes NetWork

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Attributes

Branches

Numberof Levels

Pruned

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Classification Model (cont’d)

Replaces: Multi-variate Analysis

Multiple Logistic Regression (binomial/dichotomous) Multiple Linear Regression (multiple/category)

Questions: Given a set of factors, what is the likelihood that a

disease will be expressed? What is the likelihood the disease will lead to a more

severe ailment? What category (multi-option) of health based on inputs?

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Classification Model: To Do’s

1. Create a model: Classification Build

2. Refine: Run an Attribute Importance Model to help define best attributes to “split”

3. Test the model: Classification Test

4. Predict results: Classification Apply

5. Targeting: Classification Lift

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Clustering

Unsupervised model that attempts to find groups within the population that share similar attributes

Algorithm: k-means, O-Cluster

AGE

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AGE

INCOME

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C1

C2

AGE

INCOME

C1

C2

AGE

INCOMEAge

Age

Rank

Rank

C2

C1

Centroids Histograms Courtesy Charlie Berger, Oracle

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Clustering (cont’d)

k-means only takes numeric values, and requires the number of clusters to be specified. Good for smaller datasets with fewer attributes.

O-Clusters: more robust than k-means Questions:

What groups of people are present in a population, and what are their common attributes?

How are the members distributed along those attributes? Are there given clusters of people related to a specific

disease family? Are members more or less susceptible?

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Association Models

Unsupervised model that returns a set of rules determining if one or more attributes are associated with other attributes.

Scored by support/confidence What is the likelihood of A happening if B happens? Often used with sparsely populated data sets. Questions:

What is the relationship between overweight recruits, smoking, and attrition in boot camp?

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Applications/Demos

Review of the parts of the process: JDeveloper9i layout, model wizards, creation, run ODM Browser: task review, navigation, results

Creation of models in JDeveloper9i with DM4J Wizards Clustering Model Build and analyze histograms Association Model Build: Analyze rules Classification Model: Build, Test, Apply, Lift Attribute Importance

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Challenges

Most data sources have not been modeled to collect the range of data needed.

Bio-informatics opens a whole new range of study not even imagined a few years ago.

Data Stores are inconsistent. Doctors notes are not uniform. Legacy Apps are a mess. (COBOL, poorly

documented, personnel retired…)

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More challenges

Vast amounts of data/ processing Confusion matrix on attributes with large

categories. Structuring questions “to peel away” masking

factors, and be sensitive to subtle associations Bringing it to the masses Overcoming resistance to change.

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New Native 10g Features

Text Mining – to help us search through physicians’ notes

Support Vector Machines (SVM): “Neural Networks on Steroids.”

Non-negative Matrix Factorization (NMF): Algorithm to help “boil down” many attributes into a manageable set.

Enhanced Bio-informatics support in the DB. Transformation creation (currently alpha)

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Summary

Covered a multi-disciplinary topic Attempted to show how DM is uniquely suited to

Epidemiological study Showed the ease by which models can be made Still, model creation requires trained personnel Many challenges remain to fully exploit this

technology.

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Questions?

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Special Thanks to….

Mark Kelly, Oracle Data Mining Robert Haberstoh, Oracle Data Mining

Charlie Berger, Director Oracle Data Mining

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Follow-up

Please fill out the on-line survey Session #63144

Feel free to contact me:Scott Rappoport, OCP

Principal Technical Staff MemberMTS Technologies619-725-5082rappoports@mtstech.com