Christoph F. Eick

1
Data Miningfor the Health Sciences

• Christoph F. Eick
• www.cs.uh.edu/ceick/
• ceick_at_cs.uh.edu
• University of Houston
• Organization
• 1. Health Care and Computer Science
• 2. Promising Technologies
• 2.1 KDD / Data Mining
• 2.2 Agent-based Systems
• 2.3 Shared Ontologies and Knowledge
  Brokering
• 3. Model Generation as an Example
• 4. Summary and Conclusion

2
1. Health Care and Computer Science

• Not too long ago (e.g. 1989)
• Offline data / Missing data / hand written
  reports
• Computer that cannot talk to each other
• Lack of standardization (Tower of Babel, too many
  languages)
• Today faster computers, cheaper computers,
  better computer networks, electronic scanners,
  better connectivity, the internet,...
• We have a lot of computerized knowledge on almost
  any aspects of human health(a well of knowledge)
• We have much more computing power to conduct
  complex data analysis tasks
• New Problems
• How can we find anything?
• How do we gather information that is distributed
  over various computer systems and represented
  using different formats?
• If we find something, how do we know that it is
  complete?
• How can this large amount of information be
  analyzed?
• What information can we trust?

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2. Promising Newer Technologies to Cope with
the
Information Flood

• Knowledge Discovery and Data Mining (KDD)
• Agent-based Technologies
• Ontologies and Knowledge Brokering
• Non-traditional data analysis techniques

Model Generation As an Example To Explain
/ Discuss Technologies
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Knowledge Discovery in Data and Data Mining
(KDD)
Let us find something interesting!

• Definition KDD is the non-trivial process of
  identifying valid, novel, potentially useful, and
  ultimately understandable patterns in data
  (Fayyad)
• Frequently, the term data mining is used to refer
  to KDD.
• Many commercial and experimental tools and tool
  suites are available (see http//www.kdnuggets.com
  /siftware.html)
• Field is more dominated by industry than by
  research institutions

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General KDD Steps
Data sources
Selected/Preprocessed data
Transformed data
Extracted information
Knowledge
Select/preprocess
Transform
Data mine
Interpret/Evaluate/Assimilate
Data preparation
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KDD and Classical Data Analysis

• KDD is less focused than data analysis in that it
  looks for interesting patterns in data classical
  data analysis centers on analyzing particular
  relationships in data. The notion of
  interestingness is a key concept in KDD.
  Classical data analysis centers more on
  generating and testing pre-structured hypothesis
  with respect to a given sample set.
• KDD is more centered on analyzing large volumes
  of data (many fields, many tuples, many tables,
  ).
• In a nutshell the the KDD-process consists of
  preprocessing (generating a target data set),
  data mining (finding something interesting in the
  data set), and post processing (representing the
  found pattern in understandable form and
  evaluated their usefulness in a particular
  domain) classical data analysis is less
  concerned with the the preprocessing step.
• KDD involves the collaboration between multiple
  disciplines namely, statistics, AI,
  visualization, and databases.
• KDD employs non-traditional data analysis
  techniques (neural networks, association rules,
  decision trees, fuzzy logic, evolutionary
  computing,).

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3. Generating Models as an Example

• The goal of model generation (sometimes also
  called predictive data mining) is the creation,
  evaluation, and use of models to make predictions
  and to understand the relationships between
  various variables that are described in a data
  collection. Typical example application include
• generate a model to that predicts a students
  academic performance based on the applicants data
  such as the applicants past grades, test scores,
  past degree,
• generate a model that predicts (based on economic
  data) which stocks to sell, hold, and buy.
• generate a model to predict if a patient suffers
  from a particular disease based on a patients
  medical and other data.
• Model generation centers on deriving a function
  that can predict a variable using the values of
  other variables vf(a1,,an)
• Neural networks, decision trees, naïve Bayesian
  classifiers and networks, regression analysis and
  many other statistical techniques, fuzzy logic
  and neuro-fuzzy systems, association rules are
  the most popular model generation tools in the
  KDD area.
• All model generation tools and environments
  employ the basic train-evaluate-predict cycle.

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Why Do We Need so manyData Mining / Analysis
Techniques?

• No generally good technique exists.
• Different methods make different assumptions with
  respect to the data set to be analyzed (to be
  discussed on the next transparency)
• Cross fertilization between different methods is
  desirable and frequently helpful in obtaining a
  deeper understanding of the analyzed dataset.

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Example Decision Tree Approach
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Decision Tree Approach2
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Example Nearest Neighbor Approach
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Characteristics and Assumptions of Popular Data
Mining/Analysis Techniques

• Distance based approaches (assume that a distance
  function with respect to the objects in the
  dataset exists) vs. order-based approaches (just
  use the ordering of values in their decision
  making 321 is indistinguishable from
  2.0121.99)
• Approaches that make no assumptions / assume a
  particular distribution of the data in the
  underlying dataset.
• Differences in employed approximation techniques
• Rectangular vs. other approximation
• Linear vs. non-linear approximations
• Sensitivity to redundant attributes (variables)
• Sensitivity to irrelevant attributes
• Sensitivity to attributes of different degrees of
  importance
• Different Training Performance / Testing
  Performance
• What does the learnt function tell us about the
  analyzed data set? How difficult is it to
  understand the learnt function?
• Deterministic / non-deterministic approaches
• Stability of the obtained results

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Players in the Model Generation Society
Data Analysts
Data Collection Providers
Tool Builders
End Users (Managers, Doctors, Decision Makers,
Gamblers,...)
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(More) Problems of Model Generation

• It is difficult to find appropriate data
  collections.
• Sharing of models is not supported.
• Model generation is mostly performed in a
  centralized environment, not taking advantage of
  distributed computed computing technology.
• Degree of tool standardization is low, which
  makes more difficult to use different tools for
  the same data analysis problems.
• Evaluation of claims with respect to to the
  performance models is very difficult. Problem
  the model itself, as well as tools and data
  collection that were used to generate the model
  are not accessible online.

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Key Ideas Agent-based Technologies

• Agents operate independently and anticipate user
  needs (P. Maes)
• Agent help users suffering from information
  overload (O. Etzioni) rather to mimic human
  intelligence
• Agents are important because the allow users to
  interoperate with modern applications such as
  electronic commerce and information retrieval.
  Most of these applications assume that components
  are added dynamically and that they will be
  autonomous (serve different users and providers
  to fill different goals) and heterogeneous. (M.
  Singh)
• Essentially, agent-based architectures are
  characterized by three key features autonomy,
  adaptation, and cooperation. Agent-based systems
  are computational systems in which several agents
  interact for their own good and for the good of
  the overall system.
• In an agent-based architecture services are
  provided in the context of a community of loosely
  coupled agents of various types in a distributed
  environment.
• Agents are aware of their environment and
  capable of communicating with other agents that
  belong to the same agent community.

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Simplified View of Agent-based Systems
Mediator Agents
End User Agents
Service Provider Agents
Agents that act on behalf of end users that look
for services
Agents that act as a matchmaker between service
providers and end users
Agents that act on behalf of service providers
Conversation Layer
Message Layer
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Agent-based Model Generation

• Model generation services are provided in the
  context of a community of loosely coupled agents
  of various types in a distributed environment.
• Model generation tools are accessed using a
  unified interface.
• Tool providers and data collection providers
  offer their services to data analysts and
  end-users via the internet. New forms of
  collaboration can easily be supported in this
  environment
• data analysts no longer run the tools on their
  own computing environment
• brokering techniques can be used to find
  interesting data collections, suitable tools,
  useful models, and available ontologies.
• tool developers offer tool services on the
  internet charging one-time tool use fee.

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Model Generation Agent Communities
Data Collection Provider
Resource Generation Tool
Model
Model
Data Collection
Resource Agent
Model Generation Browser
End User
Resource Agent
Data Collection Broker
Model Broker
Model Generation Browser
Data Collection
Tool Broker
Data Collection

Data Analyst
Model Generation Tool
Model Generation Tool
Agent-based Model Generation Community
Tool Developer
Tool Integration Tool
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Shared Ontologies

• Ontologies are content theories about sorts of
  objects, properties of objects, and relationship
  between objects that are possible in a specified
  domain of knowledge (Chandrasekaran)
• We consider ontologies to be domain theories
  that specify a domain-specific vocabulary of
  entities, classes, properties, predicates, and
  functions, and a set of relationships that
  necessarily hold among those vocabulary items
  (Fikes)
• Shared ontologies form the basis for domain
  specific knowledge representation languages
  (Chandrasekaran)
• If we could develop ontologies that could be
  used as the basis of multiple systems, they would
  share a common terminology that would facilitate
  sharing and reuse (W. Swartout)
• Ontologies play an important role for the
  standardization of terminology in medicine (e.g.
  UMLS) and other domains
• Ontologies can serve as the glue between
  knowledge that is represented at different,
  usually heterogeneous information sources.

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Ontologies and Brokering

• Service providers describe their capabilities in
  terms of a domain (or task) ontology
• Agents that seek services describe their needs in
  terms of a domain (or task) ontology
• Broker agents server as matchmakers between
  service providers and service seekers by finding
  suitable agents and by evaluating the extent to
  which they can provide those services relying on
  a semantic brokering approach.
• Various languages have been advocated in the
  recent years to specify ontologies OKBC,
  CKML/OML, ONTOLINGUA, XML, UMLS,...

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Promising Technologies to Use theFlood of Data
for Providing Better Health Care
Agent-based Systems KDD
Software Development Environments Knowledge Acqui
sition Tools
Visualization Traditional Data Analysis
Techniques
The Well of Knowledge
Database Technology
Ontologies
Knowledge Brokering
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References

• WWW-Links
• http//ksl-web.stanford.edu/Reusable-ontol/P001.ht
  ml (Richard Fikes (Stanford University) Slide
  Show on Reusable Ontologies
• http//www.kdnuggets.com/index.html (KDD Nuggets
  Directory Data Mining and Knowledge Discovery
  Resources)
• http//www.mcc.com/projects/infosleuth/
  (InfoSleuth (MCC) --- an Agent-based System for
  Information Gathering)
• http//www.cs.cmu.edu/softagents/ (CMU
  Intelligent Software Agents Page)
• http//www.cs.uh.edu/ceick/6368.html (Homepage
  UH Graduate AI-class)
• Papers
• Special Issue IEEE Intelligent Systems on Coming
  to Terms with Ontologies, Jan./Feb. 1999.
• Special Issue IEEE Intelligent System on
  Unmasking Intelligent Agents, March/April 1999.
• Special Issue IEEE Computer on Data Mining,
  October 1999.

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End of PresentationTransparencies that follow
are very likely not to be used
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What is KDD?

• Definition KDD is the non-trivial process of
  identifying valid, novel, potentially useful, and
  ultimately understandable patterns in data
  (Fayyad)
• The identified knowledge is used to
• make predictions
• classify new examples
• summarize the content of data collections and
  documents to facilitate understanding, decision
  making, and for supporting search and indexing
• support graphical visualization to aid human in
  discovering deeper patterns
• Example applications
• learn to classify brain tissue from examples
• predict a patients life expectancy from his
  medical history
• summarize/cluster/mine clinical trial reports

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What are Ontologies good for?

• As a shared conceptual model of a particular
  application domain that describes the semantics
  of the objects that are part of the domain, and
  captures knowledge that is inherent to the
  particular domain --- idea knowledge base .
• Ontologies provide a vocabulary for representing
  knowledge about a domain and for describing
  specific situations in a domain (tool for
  defining and describing domain-specific
  vocabularies) --- idea language for
  communication
• For data/knowledge translation and transformation
  (provide a solution to the translation problem
  between different terminologies) for fusion and
  refinement of existing knowledge --- idea
  interoperation
• For matchmaking between users, agents, and
  information resources in agent-based systems ---
  idea collaboration, brokering focus of
  next slides
• As reusable building blocks to build systems that
  solve particular problems in the application
  domain --- idea model reuse
• Summary Ontologies can be used as building
  block components of knowledge bases, object
  schema for object-oriented systems, conceptual
  schema for data bases, structured glossaries for
  human collaborations, vocabularies for
  communication between agents, class definitions
  for conventional software system, etc. (Fikes)

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Service Provider Agents
End User Agents
A Traditional Approach
Search Engine
Specify keywords with respect to the documents
they are looking for
Clinical Trial Report
Abstract Clinical Trial Report
Summary
Semantic Brokering Approach
Service Provider Agents
End User Agents
Semantic Brokering
Specify subset of ontology
Clinical Trial Report
Subset of an Ontology
Summary
matchmaking
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Why do agent-based systemsshow promise for
health care?

• Scalability
• Tasks to be solved involve the collaboration
  between different groups
• Well suited for the world-wide web
• Health care is a dynamically changing environment
  
• Establish standards (as a by product)

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Example Semantic Brokering
Data Analysts Information Requirement
Patient
Result Semantic Brokering ((DataCollection1 nil
((missing slot weight)
(contradictory ( age 40))
(DataCollection2 t) (DataCollection3 t (( age
60)( weight 300)))
Age40
weight
Intensive-Care- Patient
Hours-in-intensive-care
Data Collection1
Data Collection2
Data Collection3
Patient
Patient
Patient
Ageage
Age60
weight
Weight300
Intensive-Care- Patient
Intensive-Care- Patient
Intensive-Care- Patient
Hours-in-intensive-care
Hours-in-intensive-care
Hours-in-intensive-care
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Critical Problems with Respect to Shared
Ontologies

• Scientific communities have to agree on
  ontologies otherwise, the whole approach is
  flawed.
• Development of ontologies for a particular domain
  is a difficult task (see Digital Anatomist
  project at UW, development of UMLS). The
  development of user friendly, and intelligent
  knowledge acquisition tools is very important for
  the successful development of shared ontologies.
• Expressiveness of languages that are used to
  define ontologies limits what can be done with
  domain ontologies.
• Reasoning capabilities are important for systems
  that use shared ontologies (we need a language to
  specify ontologies and an inference engine that
  can reason with the given ontologies)
• finding inconsistencies in knowledge bases, for
  finding errors at data entry
• semantic brokering
• more intelligent mappings between terms
• ...