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0 Descision Support Systems In Medicine   Shojaee 0 Descision Support Systems In Medicine Shojaee Presentation Transcript

  • In the Name of
    the promoter
    of
    Wisdom and Beauty
  • By: Dr. Abbas Shojaee M.D., PhD stu.
    An Introduction to Decision Support Systems in Medicine
    This presentation uses works of:
    • Joseph Tan – Ehealth care information systems (book)
    • J.H. Van Bemmel - Medical informatics (book)
    • Edward H. Shortliffe – Biomedical Informatics (book)
    • Kate Farrell – Intro expert systems test (ppt file)
  • Introduction
    Every human action is the next step to data acquisition and interpretation.
    Practicing medicine is making decisions indeed.
    Computers are presumed that will help in these three steps and so decision Support in medicine.
    Due to several limitations in storage, representation and interpretation of information, it is yet limited to well structured and actionable data and knowledge.
    Questions exist:
    What?
    How?
    When?
    At what quality?
    How they affect the field?
  • Decision making in medicine
    Iterates
    Interpretation
  • Types of clinical decisions
    Clinical decisions:
    Diagnosis
    What is true?
    Diagnosis process.
    Which information is needed to make right decision
    Treatment
    Which is the best pass way through the jungle of possibilities
    Requirements:
    Accurate and adequate data.
    Applicable knowledge.
    Appropriate problem solving skills. (inference engine)
  • Some obstacles to use computers as DSS in medicine
    Abundance of rules and interacting data.
    Computational models are not as good as human inference models
    Yet we do not know the model(s) that brain uses to infer.
    Brain computational power is higher than our computers.
  • Additional terms
    Inductive and deductive reasoning
    Classification
    Feature set
    Clustering
    Decision model
    Supervised and Unsupervised learning
    Differential diagnosis
  • Expert Systems
    An expert system is a computer program that is designed to hold the accumulated knowledge of one or more domain experts
  • Applications of Expert Systems
    PUFF:
    Medical system for diagnosis of respiratory conditions
    PROSPECTOR:
    Used by geologists to identify sites for drilling or mining
  • Applications of Expert Systems
    MYCIN:
    Medical system for diagnosing blood disorders. First used in 1979
    DESIGN ADVISOR:
    Gives advice to designers of processor chips
  • Applications of Expert Systems
    DENDRAL: Used to identify the structure of chemical compounds. First used in 1965
    LITHIAN: Gives advice to archaeologists examining stone tools
  • Components of an Expert System
    The knowledge base is the collection of facts and rules which describe all the knowledge about the problem domain
    The inference engine is the part of the system that chooses which facts and rules to apply when trying to solve the user’s query
    The user interface is the part of the system which takes in the user’s query in a readable form and passes it to the inference engine. It then displays the results to the user.
  • Why use Expert Systems?
    It takes long time and great costs to train an expert.
    Experts are not always available. An expert system can be used anywhere, any time.
    Human experts are not 100% reliable or consistent
    Experts may not be good at explaining decisions
    Cost effective
  • Some Problems with Expert Systems
    Limited domain
    Systems are not always up to date, and don’t learn
    No “common sense”
    Experts needed to setup and maintain system
  • Legal and Ethical Issues
    Who is responsible if the advice is wrong?
    The user?
    The domain expert?
    The knowledge engineer?
    The programmer of the expert system shell?
    The company selling the software?
  • Medical Expert Systems
  • Codifying Human Knowledge
    • Decomposition into “chunks” of knowledge, chaining of inferences
    • Matching of case data to prototypical situations
    • Using causal models (pathophysiology) to figure out cases
  • Mycin: Rule-based Systems
    Task: Diagnosis and prescription for bacterial infections of the blood (and later meningitis)
    Method:
    Collection of modular rules
    Backward chaining
    Certainty factors
    RULE037
    IF the organism
    1) stains grampos
    2) has coccus shape
    3) grows in chains
    THEN
    There is suggestive evidence (.7) that the identity of the organism is streptococcus.
  • Mycin Consult
    --------PATIENT-1--------
    1) Patient's name: FRED SMITH
    2) Sex: MALE
    3) Age: 55
    4) Have you been able to obtain positive cultures from a site at which Fred Smith has an infection? YES
    --------INFECTION-1--------
    5) What is the infection? PRIMARY-BACTEREMIA
    6) Please give the date when signs of INFECTION-1 appeared. 5/5/75
    The most recent positive culture associated with the primary-bacteremia will be referred to as:
    --------CULTURE-1--------
    7) From what site was the specimen for CULTURE-1 taken? BLOOD
    8) Please give the date when this culture was obtained. 5/9/75
    The first significant organism from this blood culture will be called:
    --------ORGANISM-1--------
    9) Enter the identity of ORGANISM-1. UNKNOWN
    10) Is ORGANISM-1 a rod or coccus (etc.)? ROD
    11) The gram stain of ORGANISM-1: GRAMNEG
    . . .
    Davis, et al., Artificial Intelligence 8: 15-45 (1977)
  • How Mycin Works
    • Dynamically traces out behavior of (what might be) a flowchart
    • Information used everywhere appropriate
    • Single expression of any piece of knowledge
    • To find out a fact
    • If there are rules that can conclude it, try them
    • Ask the user
    • To “run” a rule
    • Try to find out if the facts in the premises are true
    • If they all are, then assert the conclusion(s), with a suitable certainty
    • Backward chaining from goal to given facts
  • Explore Mycin’s Use of Knowledge
    ** Did you use RULE 163 to find out anything about ORGANISM-1?
    RULE163 was tried in the context of ORGANISM-1, but it failed because it is not true that the patient has had a genito-urinary tract manipulative procedure (clause 3).
    ** Why didn't you consider streptococcus as a possibility?
    The following rule could have been used to determine that the identity of ORGANISM-1 was streptococcus: RULE033
    But clause 2 (“the morphology of the organism is coccus”) was already known to be false for ORGANISM-1, so the rule was never tried.
    Davis, et al., Artificial Intelligence 8: 15-45 (1977)
  • Even Simpler Representation
    Disease
    Disease
    s1
    s1
    s2
    s2
    s3
    s3
    s4
    s4
    s5
    s5
    s6
    s6
    s7
    s7
    s8
    s8
    s9
    s9
    s10
    s10
    s...
    s...
  • Diagnosis by Card Selection
    Disease
    Disease
    Disease
    s1
    s1
    s1
    s2
    s2
    s2
    s3
    s3
    s3
    s4
    s4
    s4
    s5
    s5
    s5
    s6
    s6
    s6
    s7
    s7
    s7
    s8
    s8
    s8
    s9
    s9
    s9
    s10
    s10
    s10
    s...
    s...
    s...
    Disease
    s1
    s2
    s3
    s4
    s5
    s6
    s7
    s8
    s9
    s10
    s...
  • Diagnosis by Edge-Punched Cards
    Dx is intersection of sets of diseases that may cause all the observed symptoms
    Difficulties:
    Uncertainty
    Multiple diseases
    “Problem-Knowledge Coupler” of Weed
  • Multi-Hypothesis Diagnosis
    Set aside complementary hypotheses
    … and manifestations predicted by them
    Solve diagnostic problem among competitors
    Eliminate confirmed hypotheses and manifestations explained by them
    Repeat as long as there are coherent problems among the remaining data
  • Internist/QMR
    • Knowledge Base:
    • 956 hypotheses
    • 4090 manifestations (about 75/hypothesis)
    • Evocation like P(H|M)
    • Frequency like P(M|H)
    • Importance of each M
    • Causal relations between H’s
    • Diagnostic Strategy:
    • Scoring function
    • Partitioning
    • Several questioning strategies
  • QMR Database
  • QMR Scoring
    Positive Factors
    Evoking strength of observed Manifestations
    Scaled Frequency of causal links from confirmed Hypotheses
    Negative Factors
    Frequency of predicted but absent Manifestations
    Importance of unexplained Manifestations
    Various scaling parameters (roughly exponential)
  • Example Case
  • Initial Solution
  • More Expert Systems
    Causality?
    What’s in a Link?
    Temporal reasoning
    Quantitative reasoning
    Model-based reasoning
    Workflow
  • Meaning of Representation?
    causes
    S
    D
    Always? probability
    Magnitude? severity; bad cold  worse fever?
    Delay? temporality
    Where? spatial dependency
    Under what conditions? context
    Interaction of multiple causes physical laws
    Cross-terms high-dimensional descriptions
  • Interpreting the Pastwith a Causal/Temporal Model
  • Exploiting Temporal Relations
    abdominal
    pain
    ?
    blood
    transfusion
    ?
    jaundice
    transfusion precedes both abdominal pain and jaundice implies transfusion-borne acute hepatitis B
    as in 1, but only by one day
    jaundice occurred 20 years ago, transfusion and pain recent
    Can be very efficient at filtering out nonsense hypotheses.
  • The “Too Many Rules” Problem
    One decision support rule may work very well.
    10 rules may work better
    1000 rules may be unworkable!
    Need for a comprehensive “view” of the medical status of the patient
    Alert based on a full set of diagnoses not individual facts
  • Conclusions
    Medical decision support can be helpful in reducing medical errors
    Usually based on simple rule based systems
    More advanced medical decision support systems can be use to help with diagnosis
    Not much use of advanced systems at present in routine care