Oracle%20Data%20Mining%20and%20Epidemiological%20Analysis

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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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Lets 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
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The Data Mining Side
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Reporting techniques/hierarchies
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Reporting Examples
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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 (contd)

• 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
• Model
• Test (obtain false-positives/negatives
• Apply
• Lift
• Attribute Importance
• Determine attributes with the most effect on
  result
• Want 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?
• Associations
• Find 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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Classification Model (contd)

• 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 Dos

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

Courtesy Charlie Berger, Oracle
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Clustering (contd)

• 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 Member
• MTS Technologies
• 619-725-5082
• rappoports_at_mtstech.com