0 descision support systems in medicine - shojaee
DESCRIPTION
DSS in healthcareTRANSCRIPT
In the Name of
the promoter
of
Wisdom and Beauty
BY: DR. ABBAS SHOJAEE M.D.
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
Acquisition of data
Linking to data context to form
meaningful information
Deciding on next required
information and requesting them
Trying to reduce possibilities to
form an actionable hypothesis
Action: making therapy orDeciding on
next diagnostic step Interpretation
Itera
tes
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 modelsYet 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 reasoningClassificationFeature setClusteringDecision modelSupervised and Unsupervised learningDifferential 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
PROSPECTOR:Used by geologists to
identify sites for drilling or mining
PUFF:Medical system
for diagnosis of respiratory conditions
Applications of Expert Systems
DESIGN ADVISOR:Gives advice to designers of
processor chips
MYCIN:Medical system for diagnosing blood
disorders. First used in 1979
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 domainSystems are not always up
to date, and don’t learnNo “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 organism1) stains grampos2) has coccus shape3) grows in chains
THENThere is suggestive evidence (.7) that the identity of the organism is streptococcus.
Mycin Consult
--------PATIENT-1--------1) Patient's name: FRED SMITH2) Sex: MALE3) Age: 554) 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-BACTEREMIA6) Please give the date when signs of INFECTION-1 appeared. 5/5/75The 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? BLOOD8) Please give the date when this culture was obtained. 5/9/75The first significant organism from this blood culture will be called:
--------ORGANISM-1--------9) Enter the identity of ORGANISM-1. UNKNOWN10) Is ORGANISM-1 a rod or coccus (etc.)? ROD11) The gram stain of ORGANISM-1: GRAMNEG. . .
Davis, et al., Artificial Intelligence 8: 15-45 (1977)
How Mycin Works
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
Dynamically traces out behavior of (what might be) a flowchart Information used everywhere
appropriate Single expression of any piece of
knowledge
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: RULE033But 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
s1s2s3s4s5s6s7s8s9s10s...
Disease
s1s2s3s4s5s6s7s8s9s10s...
Diagnosis by Card Selection
Disease
s1s2s3s4s5s6s7s8s9s10s...
Disease
s1s2s3s4s5s6s7s8s9s10s...
Disease
s1s2s3s4s5s6s7s8s9s10s...
Disease
s1s2s3s4s5s6s7s8s9s10s...
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 themSolve diagnostic problem among competitorsEliminate confirmed hypotheses and
manifestations explained by themRepeat 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
HypothesesNegative 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 reasoningQuantitative reasoningModel-based reasoningWorkflow
Meaning of Representation?
Always? probabilityMagnitude? severity; bad cold worse fever?Delay? temporalityWhere? spatial dependencyUnder what conditions? contextInteraction of multiple causes physical lawsCross-terms high-dimensional descriptions
SDcauses
Interpreting the Pastwith a Causal/Temporal Model
weak heart
heart failure
digitalis effect
retain
losediuretic effect
high
low
edemafluid therapy
water blood volume
low cardiac output
definite cause
possible cause
possible correction (not all shown)
Exploiting Temporal Relations
transfusion precedes both abdominal pain and jaundice implies transfusion-borne acute hepatitis B
as in 1, but only by one dayjaundice occurred 20 years ago, transfusion
and pain recentCan be very efficient at filtering out
nonsense hypotheses.
bloodtransfusion
abdominalpain
jaundice
?
?
The “Too Many Rules” Problem
One decision support rule may work very well.
10 rules may work better1000 rules may be unworkable!Need for a comprehensive “view” of the
medical status of the patientAlert 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 systemsMore advanced medical decision support
systems can be use to help with diagnosisNot much use of advanced systems at present
in routine care