Christoph F. Eick’s Areas of Interest Knowledge Discovery in Data and Data Mining (KDD) – Expertise in developing and using data mining techniques and tools --- mostly for structured data collections (also started some work concerning images) – Database Clustering / Generalizing Data Mining Techniques for Databases – Preprocessing in KDD – Constructive Induction, Symbolic Regression, and Genetic Programming Agent-based Technologies – Ontologies and Semantic Brokering – The InfoSleuth Information Gathering System – Integration of Agent-based Technologies and Knowledge Discovery/Data Mining Knowledge-based Systems, Expert Systems, and [Knowledge Acquisition] Using Bayesian Technology to Assist Decision Making (in Medicine and other domains) Computerization of Medical Practice Guidelines Genetic Programming and Evolutionary Techniques Sound background in Data Models, Databases, and AI Data Mining for the Health Sciences, Seattle, 4/27/99. Data Mining for the Health Sciences Christoph F. Eick www.cs.uh.edu/~ceick/eick-uw.html [email protected] 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. Summary and Conclusion Data Mining for the Health Sciences, Seattle, 4/27/99. 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…) Human is frequently the “gold standard” 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? Data Mining for the Health Sciences, Seattle, 4/27/99. Promising “Newer” Technologies to Cope with the Information Flood Knowledge Discovery and Data Mining (KDD) Agent-based Technologies Shared Ontologies and Knowledge Brokering Non-traditional data analysis techniques Structural Search and Indexing Techniques Data Mining for the Health Sciences, Seattle, 4/27/99. 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 Data Mining for the Health Sciences, Seattle, 4/27/99. 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 patient’s life expectancy from his medical history – summarize/cluster/mine clinical trial reports Data Mining for the Health Sciences, Seattle, 4/27/99. General KDD Steps Data sources Selected/Preprocessed data Select/preprocess Transform Transformed data Extracted information Knowledge Data mine Interpret/Evaluate/Assimilate Data preparation Data Mining for the Health Sciences, Seattle, 4/27/99. 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, decision trees, fuzzy logic, evolutionary computing,…). Data Mining for the Health Sciences, Seattle, 4/27/99. 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”. Data Mining for the Health Sciences, Seattle, 4/27/99. Simplified View of Agent-based Systems End User Agents Agents that act on behalf of end users that look for services Mediator Agents Service Provider Agents Agents that act as a matchmaker between service providers and end users Agents that act on behalf of service providers Conversation Layer Message Layer Data Mining for the Health Sciences, Seattle, 4/27/99. A few more things on Agents Why do agent-based systems show 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) Third International Conference on AUTONOMOUS AGENTS (Agents '99), Seattle, Washington, May 1-5, 1999 (http://www.cs.washington.edu/research/agents99/) Data Mining for the Health Sciences, Seattle, 4/27/99. Generating Models 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 student’s academic performance based on the applicants data such as the applicant’s 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 patient’s medical and other data . Neural networks, decision trees, naïve Bayesian classifiers and networks, many other statistical techniques, fuzzy logic and neuro-fuzzy systems are the most popular model generation tools in the KDD area. All model generation tools and environments employ the basic train/evaluate/predict cycle. Data Mining for the Health Sciences, Seattle, 4/27/99. Participants in an Agent-based Data Analysis / KDD Society Data Analysts Data Collection Providers Tool Builders End Users (Managers, Doctors, Decision Makers, Gamblers,...) Data Mining for the Health Sciences, Seattle, 4/27/99. 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. Data Mining for the Health Sciences, Seattle, 4/27/99. 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 onetime tool use fee. Data Mining for the Health Sciences, Seattle, 4/27/99. Model Generation Agent Communities Data Collection Provider Resource Generation Tool Model Model Data Collection Model Generation End User Browser Resource Agent Resource Agent Model Generation Browser Data Collection Broker Model Broker Tool Broker Data Analyst Model Generation Tool Tool Integration Tool Model Generation Tool Tool Developer Data Collection Data Collection Agent-based Model Generation Community Data Mining for the Health Sciences, Seattle, 4/27/99. 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.” Data Mining for the Health Sciences, Seattle, 4/27/99. 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) Data Mining for the Health Sciences, Seattle, 4/27/99. 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,... Data Mining for the Health Sciences, Seattle, 4/27/99. Service Provider Agents A “Traditional” Approach End User Agents Specify keywords with respect to the documents they are looking for Search Engine Abstract Clinical Trial Report Summary Clinical Trial Report Semantic Brokering Approach Service Provider Agents End User Agents Semantic Brokering Specify subset of ontology Subset of an Ontology Summary Clinical Trial Report := matchmaking Data Mining for the Health Sciences, Seattle, 4/27/99. Example Semantic Brokering Data Analyst’s Information Requirement Patient Age>40 weight Intensive-CarePatient Hours-in-intensive-care Data Collection1 Result Semantic Brokering: ((DataCollection1 nil ((missing slot weight) (contradictory (< age 15) (> age 40)) (DataCollection2 t) (DataCollection3 t ((> age 60)(> weight 300))) Data Collection2 Data Collection3 Patient Patient Patient Age<15 age Age>60 weight Intensive-CarePatient Hours-in-intensive-care Intensive-CarePatient Hours-in-intensive-care Weight>300 Intensive-CarePatient Hours-in-intensive-care Data Mining for the Health Sciences, Seattle, 4/27/99. 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 – ... Data Mining for the Health Sciences, Seattle, 4/27/99. Promising Technologies to Use the Flood of Data for Providing Better Health Care Agent-based Systems Structural Indexing Techniques Software Development Environments Knowledge Acquisition Tools KDD The Well of Knowledge Database Technology Visualization Traditional Data Analysis Techniques Shared Ontologies Semantic Brokering Data Mining for the Health Sciences, Seattle, 4/27/99. References WWW-Links: – http://www.nlm.nih.gov/pubs/cbm/umlscbm.html (UMLS) – http://ksl-web.stanford.edu/Reusable-ontol/P001.html (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) 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 Communications of the ACM on “Data Mining”, vol. 39, no. 11, November 1996. Data Mining for the Health Sciences, Seattle, 4/27/99.