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My Research Areas

My Research Areas. Xudong William Yu Department of Computer Science Southern Illinois University Edwardsville. Overview. Artificial Intelligence (most likely to chair) Model-based Reasoning and Diagnosis Expert Systems Integrated Systems AI in Education: Robotics, Learning-by-Teaching

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My Research Areas

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  1. My Research Areas Xudong William Yu Department of Computer Science Southern Illinois University Edwardsville

  2. Overview • Artificial Intelligence (most likely to chair) • Model-based Reasoning and Diagnosis • Expert Systems • Integrated Systems • AI in Education: Robotics, Learning-by-Teaching • Database: • Data Warehouse (could chair) • AI+DB: Data Stream Mining (will chair)

  3. Some Projects • HDA (Hypertension Decision Aid) • MDS (Multi-level Diagnosis System) • MIDST (Mixed Inference Dempster-Shafer Tool) • DOC (Diagnosis Of Complex systems) • Betty the Brain

  4. HDA Background Chronic Hypertension • High-blood pressure • 140/90 mmHg or higher • Approximately 50 million hypertensive in the U.S. • Number one reason a patient in this country seeks medical care • Number one reason physicians in this country prescribe medication

  5. Background Primary Hypertension • “Primary” – no specific cause, contributing factors: • Genetics • Diet & Life style • Personality type • “Chronic” – requires a life time of treatment • Treatments include medications, diet changes, life style changes

  6. Background • Risks of untreated hypertension • Stroke • Heart Attack • Heart Failure • Kidney Failure • Cardiovascular Disease • Measuring the effectiveness of treatments depends on obtaining regular and accurate measurements of a patient’s blood pressure

  7. Physicians: Listen carefully to patient concerns Take accurate & complete medical histories Help patients understand their medical problems Communicate treatment recommendations & medical advice Patients: Relay medical history Accurately report signs & symptoms Voice concerns & questions about conditions & treatments Motivation:Communication is the cornerstone of medical practice Poor communication is a major cause of misdiagnosis, poor compliance of therapy, and malpractice claims.

  8. Communication Characteristics of HDA • Asynchronous – does not require appointments or time off • Push Medium – does not require a special activity to find information • Directed Conversation – relevant & personalized content to the patient’s condition

  9. HDA System Architecture

  10. Patient Interface Used to record and report patients’ vital signs and stats Physician Interface Used to monitor patient status and display trend graphically System Interfaces

  11. Main Components of HDA • Decision Module (DM) • Use expert system technology to provide decision support to physicians. • Treatment Database (TD) • Stores the medical records of patients. • Rule Editing and Verification (REV) Tool • Provides online assistance to patients.

  12. Decision Module Main functions: • Monitor the TD • Evaluate the treatment plans of patients • Suggest changes when necessary • Implementation: • FreeShell Live two senior projects

  13. Decision Module Three operating modes: 1. Routine Evaluation • Periodical evaluation treatment plans • Basic steps: • Query the database for the latest data • Derive a list of medications ranked by Certainty Factor (CF) values • Takes appropriate action based on the difference in CF value between current medication and the top-ranked medication: • Case 1: <0.2, no action • Case 2: 0.2 – 0.5. Inform the primary care physician. • Case 3: >0.5, Advice the patient to contact primary care physician immediately

  14. Decision Module 2. Emergency Evaluation • Triggered by changes in a patient’s vital signs. • Evaluation is similar to routine evaluation. • Inform the primary care physician immediately. 3. Online consultation • Initiated by the primary care physician • Complete evaluation with all test results

  15. Decision Module Knowledge Base • Based on “The Sixth Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure” • Use production rules: Conditions  Conclusion CF, Conclusion CF, … Ex., Total-cholesterol = Above-Normal or Total-cholesterol = Increasing Conclude: Medication = Thiazide -600 Medication = Loop-Riuretics -600

  16. An Example Rule

  17. Knowledge Engineering • Medications are are ranked by CF values • Certainty Factors are combined and fine tuned • A small subset 

  18. The REV Tool Knowledge Acquisition in DM • A GUI for editing rules • Provide a vocabulary to reduce syntactic errors. • Perform Front-end knowledge base verification. • Test for completeness with cases selected based their relevance.

  19. REV Interface

  20. Knowledge Base Verification • Local inconsistencies and incompleteness are detected immediately. • Example of local incompleteness • Unusable rules: • Contains conditions that are not verifiable. • Dead-end rules: • Not in a path to a conclusion.

  21. Knowledge Base verification Categories of local inconsistencies: • Redundant rules: • A  B & C • A  B & D • Contradictory rules • A  (C 0.7) • A  B & (C –0.5) • Over-specified conditions: • A  C • A & B  C • Circular rules: • A  B & C • C  D • D  A & E

  22. HDA Summary • Supplement the physician & patient relationship with greater interaction • Help the physician in patient monitoring and decision making. • Personalizes the content of the interaction similar to direct visitations • Provides patients an active role in their health care & immediate feedback (“plug-in” feeling)

  23. Some Related Research • Diagnosis • Model based vs. Associational • Modeling for Diagnosis • Database Design • Extending UML Class Diagram

  24. Model Based Diagnosis Three Subtasks • Candidate generation • Candidate testing • Candidate discrimination

  25. Conflict: Components involved in the discrepancy Example: (M1, M2, A1) Diagnosis = Blame Assignment

  26. Model Based Diagnosis • Expert system approach uses associational knowledge • Considered “shallow” & “Brittle” • Model-based diagnosis • Based on a model of the system • Tend to be more complete • Key: an adequate model

  27. Pneumatic System

  28. A Method for Candidate Generation

  29. Example Equation Model

  30. Causal analysis on the equations To relate change in output parameter to changes in component • Compute partial derivatives on the equations for X • Perform sign analysis, since A, k, E, Fs are +, the partial derive is -, thus PDC(X, Rs-) = + • Through propagation, we obtain PDC(Tho , Rs-) = +

  31. Generation Partial Conflicts • Current OBS: Tho above normal • Partial derivative for Tho • Partial conflict for Tho: PC(Tho+) = Rp+  Ef+  K-  Rs-  Ec Rcs-

  32. An Integrated Architecture

  33. Some Future Work • Methods for pattern recognition from online, continuous data stream • Methods for modeling of continuous systems • Create modeling and analysis tools • Knowledge Acquisition: ex., SQL  Rules • GUI Tools

  34. Questions?

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