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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 Database: Data Warehouse (could chair) AI+DB: Data Stream Mining (will chair) 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 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 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 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 Motivation: Communication is the cornerstone of medical practice 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 Poor communication is a major cause of misdiagnosis, poor compliance of therapy, and malpractice claims. 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 HDA System Architecture System Interfaces Patient Interface Used to record and report patients’ vital signs and stats Physician Interface Used to monitor patient status and display trend graphically 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. Decision Module Main functions: Monitor the TD Evaluate the treatment plans of patients Suggest changes when necessary Implementation: FreeShell Live two senior projects 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 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 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 An Example Rule Knowledge Engineering Medications are are ranked by CF values Certainty Factors are combined and fine tuned A small subset CO SVR Male P14 36-45 P14 >65 M5 L19 LDL L7 HDL Initial T11 E 0.5 -0.7 -0.8 0.3 T12 E 0.5 -0.7 -0.8 0.2 T13 E 0.5 -0.7 -0.8 T21 E 0.6 -0.6 -0.8 -0.8 0.3 T22 E 0.1 -0.8 0.2 T23 E 0.1 -0.8 0.2 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. REV Interface 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. Knowledge Base verification Categories of local inconsistencies: Redundant rules: Contradictory rules A (C 0.7) A B & (C –0.5) Over-specified conditions: AB &C AB &D AC A&BC Circular rules: AB &C CD DA&E 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) Some Related Research Diagnosis Model based vs. Associational Modeling for Diagnosis Database Design Extending UML Class Diagram Model Based Diagnosis Three Subtasks Candidate generation Candidate testing Candidate discrimination Diagnosis = Blame Assignment Conflict: Components involved in the discrepancy Example: (M1, M2, A1) 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 Pneumatic System A Method for Candidate Generation Example Equation Model v l Tho Thi Ch (Thi Tci )( ) l Cc Rc D 2 2 R l cR p p D CcChR p2 2 (CcRc( Rh Rp) ChRh( Rc Rp)) ( Rc Rh Rp) RhRcl RcRh v l C c Vc tFc c Fc c v A X ( Pro Rs Fs ) E k E E f Ec (Tset Rcs Qr ) X Rs A Fs E k Causal analysis on the equations To relate change in output parameter to changes in component Compute partial derivatives on the equations for X X A Rs k Fs E Perform sign analysis, since A, k, E, Fs are +, the partial derive is -, thus PDC(X, Rs-) = + Through propagation, we obtain PDC(Tho , Rs-) = + Tho Tho Cc Fc X l l (Ch (Thi Chi ) 2 ) ( c ) ( Rs v Cc Fc X Rs Cc Rc D P3 C3 ) ( A Fs E ) k Generation Partial Conflicts Current OBS: Tho above normal Partial derivative for Tho Tho Tho Cc Fc X l l (Ch (Thi C hi ) 2 ) ( c ) ( Rs v Cc Fc X Rs Cc Rc D P3 C3 ) ( A Fs E ) k Partial conflict for Tho: PC(Tho+) = Rp+ Ef+ K- Rs- Ec Rcs- An Integrated Architecture Knowledge-Base Editor User Interface Model-Based Diagnoser Associational Module Knowledge Base Inference Engine Model Builder Diagnostic Controller MBD Module 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