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Victor Babes UNIVERSITY OF MEDICINE AND
PHARMACY TIMISOARA

• DEPARTMENT OF
• MEDICAL INFORMATICS AND BIOPHYSICS
• Medical Informatics Division
• www.medinfo.umft.ro/dim
• 2004 / 2005

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MEDICAL DECISION SUPPORT (II)

• COURSE 12

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4. ELEMENTS OF LOGICS 4.1. GENERAL NOTIONS

• a) SENTENCE
• expressing ideas about different objects or
  events features or causes
• TYPES cognitive, interogative, imperative...
• TRUTH VALUE
• True (T), False (F), Uncertain (?)
• b) LOGIC FORMS
• notion, sentence, inference
• logic form of a declarative affirmative sentence
  S is P (Ssubject, Ppredicate)

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• c) PRINCIPLES OF LOGICS
• identity principle
• non-contradiction principle
• excluded tertiary principle
• sufficient rationale principle
• d) CATEGORICAL SENTENCES
• Universal - affirmative/negative
• All S are P. None S is P.
• Particular - affirmative, negative
• Some S are P. Some S are not P.

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4.2. COMPOSED SENTENCES

• Applying an operator on one or two simple
  sentences
• (unary or binary operators)
• Truth value of composed sentences
• Negation p 1 0
• (not) p 0 1
• Conjunction q p 1 0
• (p Ù q) 1 1 0
• (and) 0 0 0

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• Disjunction q p 1 0
• (p Ú q) 1 1 1
• (or) 0 1 0
• Implication q p 1 0
• (p q) 1 1 1
• (ifthen...) 0 0 1

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• Exclusive q p 1 0
• disjunction 1 0 1
• (oror...) XOR 0 1 0
• Equivalence q p 1 0
• (p q) 1 1 0
• (if and only if) 0 0 1

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4.3. LOGICAL INFERENCE

• Structure premises (2 sen) conclusion
• Modus (p q) q
• ponens p
• Modus (p q) p
• tollens q
• Sillogism (p q) (p r) (q r)

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4.4. Ex PROLOG LANGUAGE

• domains
• person, activity symbol
• predicates
• likes (person, activity)
• clauses
• likes (ellen, tennis)
• likes (tom, baseball)
• likes (bill, X) if likes (tom, X)
• RUN goal likes (bill, baseball)
• TRUE

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5. HEURISTIC METHODS EXPERT SYSTEMS 5.1. SCHEME
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5.2. COMPONENTS

• a) COGNITIVE SYSTEM
• KNOWLEDGE BASE 3 levels
• FACTUAL
• CONCEPTUAL
• (meta-knowledge)

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• b) REASONING SYSTEM (inference machine)
• c) COMMUNICATION SYSTEM (user interface)
• d) EXPLANATORY SYSTEM
• e) Meta-resolutive system
• to check if results are valid and reasoning is
  adequate)

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5.3. COGNITIVE SYSTEM AND KNOWLEDGE BASE

• Medical knowledge extraction
• general knowledge
• knowledge from clinical experience
• Medical knowledge formalization
• KB may be extended
• Self-completion (PROLOG language appropriate
  for Artificial Intelligence)
• May be used either for decision support or for
  educational purposes)

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• 5.4. REASONING SYSTEM
• leading position - drives the dialogue
• may accept statistical procedures
• 5.5. EXPLANATORY SYSTEM
• shows the reasoning trace
• educational purposes
• 5.6. COMMUNICATION SYSTEM
• natural language use

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5.7. MEDICAL Expert Systems

• MYCIN - bacterial infections
• PUFF - pulmonary deseases
• HEADMED - neuro-psychiatry
• CASNET - ophtalmology
• VM -ventilator monitor
• INTERNIST - internal medicine
• TROPICAID - tropical diseases
• Domain-independent E.S.

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6. CLASSIFICATION QUALITYASSESSMENT
a) Classification table Classifier
D D-
D N11 N12 R1 D- N21 N22 R2
C1 C2 N
Real
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b) PARAMETERS

• false negative N12
• false positive N21
• sensitivity N11 / R1 (capacity to detect the
  diseased)
• specificity N22 / R2 (capacity to reject the
  non-diseased)
• inverse relationship between sensitivity and
  specificity
• positive predicitve value N11 / C1
• negative predictive value N22 / C2
• accuracy (N11 N22) / N
• classification error rate (N12 N21) / N
• ROC (Receiver Operating Characteristics)
• SN f (1 SP)

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c) Example

• A study was carried on a population of 3000
  individuals. 500 of them had virosis during last
  year. Our computer program was able to detect 480
  of them, but gave the same diagnosis to another
  50 persons. Compute
• number of false positives and false negatives
• sensitivity and specificity
• accuracy and classification error rate
• positive and negative predictive rate

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End