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The Representation of Medical Reasoning Models in Resolution-based Theorem The Representation of Medical Reasoning Models in Resolution-based Theorem

The Representation of Medical Reasoning Models in Resolution-based Theorem - PowerPoint Presentation

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The Representation of Medical Reasoning Models in Resolution-based Theorem - PPT Presentation

Provers Originally Presented by Peter Lucas Department of Computer Science Utrecht University Presented by Sarbartha Sengupta 10305903 Megha Jain 10305028 Anjali Singhal 10305919 ID: 927417

representation reasoning models medical reasoning representation medical models theorem based logic artificial intelligence resolution peter lucas provers knowledge lesion

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Slide1

The Representation of Medical Reasoning Models in Resolution-based Theorem Provers

Originally Presented byPeter LucasDepartment of Computer Science, Utrecht University

Presented

by

Sarbartha

Sengupta

(10305903)

Megha

Jain (10305028

)

Anjali

Singhal

(10305919)

(14

th

Nov, 2010)

Slide2

Introduction

Several common reasoning models in medicine are being investigated, familiar from the AI literature.

The mapping of those models to logical representation is being investigated.

The purpose of translation is to obtain a representation that permits

automated interpretation by a Logic-based Theorem

Prover

.

Slide3

Medical Reasoning Models

Diagnostic

Anatomical

Causal Reasoning

Slide4

Logic as a language for representation of medical knowledge.

First order predicate logic: language to express knowledge concerning objects and relationship between objects.

Motivation

Slide5

Logic: One of the major candidate of knowledge representation language in future expert system.Most other knowledge-representation languages are not completely understood.

Logic is the unifying framework for integrating expert systems and database systems.

Slide6

Hypotheses

The use of logic language: Revel the underlying structure of a given medical problem.

First order logic – sufficiently flexible for the representation of a significant fragment of medical knowledge.

Slide7

P(t1,t2

,…,tn)

P : relation

t

i

:

o

bjects

First Order Logic

Slide8

P(t1,t2

,…,tn)

P : relation

t

i

:

o

bjects

Atom

Individual Object

Constant

Class of Objects

Variable

Dependencies upon other Objects

Function

First Order Logic

Slide9

In logic-based Theorem Prover, the syntax of formulae is restricted to clausal form.

Clause: a finite disjunction literals.Literals: an atom (positive literals)

or negation of an atom (negative literals)

Horn clause: contains at least one positive negation.

Null clause :

Slide10

Logic Data Representation in Medicine

Individual Objects : patients, substances …

Properties of the objects

: physiological states, level of substances …

Single Valued

: Unique at a certain point of time.

Multi Valued

: Several fill-ins may occurs at the same time.

Age(

johnson

) = 30

Sign(

johnson

,

jundice

)

Sign(

johnson

,

spider_angiomas

)

Slide11

Medical Reasoning Models

Diagnostic

Anatomical

Causal Reasoning

Slide12

Diagnostic Reasoning

Logical representation of diagnostic reasoning is viewed as a deductive process instead of abductive processAspects of formalization of medical diagnostic reasoning:

Some suitable logical representation of patient data must be chosen.

We have to decide on the logical representation of diagnostic medical knowledge.

Slide13

Attempt to reformulate the HEPAR system.HEPAR System: a rule based expert system for the diagnosis of disorders of liver and biliary tract.

Slide14

sex (patient1 ) = femaleage(patient1

) = 12Complaint(patient1,arthralgia )time course(patient1,illness ) = 150

...

Signs(patient1,Kayser Fleischer rings)

...

ASAT(patient1,labresult,biochemistry ) = 200

urinary

copper (patient1,labresult,biochemistry ) = 5

...

In this

case, the representation language is primarily viewed as a term manipulation language

,

not as a logical language.

Slide15

patient (name = patient1 ;sex = female;age = 12;...

complaint = [arthralgia ];...)The representation of patient data in logic seems straightforward.

Slide16

Diagnostic medical knowledge is represented in HEPAR system using production rules. Object-attribute-value

According to the declarative reading of rules,

Slide17

Diagnostic medical knowledge is represented in HEPAR system using production rules.

Object-attribute-value

According to the declarative reading of rules,

Translation of most production rules is straightforward.

Example taken from: Peter

Lucas, The Representation of Medical Reasoning Models in Resolution-based Theorem

Provers

, Artificial Intelligence

Slide18

More than 50% of the production rules in the HEPAR system could only be represented in non-Horn clauses.So, a Horn-Clause based Theorem

Prover is insufficient.

Diagnostic reasoning in medicine typically involves reasoning about diagnostic categories.

Slide19

The data of a specific patient represented as A collection of unit clause D,The diagnostic theory T

The diagnostic problem solving can be established as

Resolution based Theorem

Prover

x

: patient name.

y

: possible discloser.

Slide20

Anatomical Reasoning

Automated reasoning in which knowledge concerning the anatomy of the human body is applied.Point of departure is the axiomatization of the basic anatomical relations.

Slide21

Only certain anatomical structures are connected to each other in a qualitative way.This is axiomated by the connected predicate.Connected predicate is defined as a transitive, irreflexive relation :

∀x ∀y ∀z(connected(x , y) ∧ connected(y , z) → connected(x , z)) ∀x(⌐connected(x , x))

Slide22

Formalization of Knowledge base for Facial Palsy disease : This is paralysis of part of the face caused by non-functioning of the nerve that controls the muscles of the face. This nerve is called the facial nerve.

Image taken from: Peter

Lucas, The Representation of Medical Reasoning Models in Resolution-based Theorem

Provers

, Artificial Intelligence

Slide23

Axiomatization of anatomical relationships by giving a domain specific fill-in for connected predicate. It means facial nerve runs from level x up to level y.

connected(x , y)

Slide24

Relation between anatomical structures and signs that may arise due to facial nerve lesion. Signs associated with a lesion at certain level x includes all the signs of a lesion at a lower level y.

∀x∀y ( Lesion( x ) ∧ Connected(y , x) → Lesion( y ) )

Slide25

Relation between a lesion at a certain level and the specific anatomical structures that will be affected by the lesion affected by the lesion, expressed by the unary predicate Affected.

(Lesion(level) ↔ (Affected(structure 1) ∧ Affected(structure 2) ∧….Affected(structure n)))

Slide26

Relation between structure affected and specific signs and complaints for this. (Affected(structure) ↔ (sign(x₁) ∧ sign(x₂)

∧….sign(xₐ))) (Affected(structure) ↔ (complaint(x₁) ∧ complaint(x₂)

….complaint(xₐ)))

Slide27

Using this Logical theory Expert system can derive: For a level the values corresponding to x and y can be calculated using the knowledge base.

T ∪ { Lesion(level)} ∪ {⌐Sign( x )} ∪ {⌐Complaint( y ) } ⊢ □

Slide28

Connected predicate for facial nerve:

Example taken from: Peter Lucas, The Representation of Medical Reasoning Models in Resolution-based Theorem Provers

, Artificial Intelligence

Slide29

Relation between anatomical structures and signs that may arise due to facial nerve lesion.

Example taken from: Peter Lucas, The Representation of Medical Reasoning Models in Resolution-based Theorem Provers

, Artificial Intelligence

Slide30

Example taken from: Peter

Lucas, The Representation of Medical Reasoning Models in Resolution-based Theorem Provers, Artificial Intelligence

Slide31

Relation between structure affected and specific signs and complaints for this.

Example taken from: Peter

Lucas, The Representation of Medical Reasoning Models in Resolution-based Theorem

Provers

, Artificial Intelligence

Slide32

Example taken from: Peter

Lucas, The Representation of Medical Reasoning Models in Resolution-based Theorem Provers, Artificial Intelligence

Slide33

For x we have mouth_droops, cannot_whistle, cannot_close_eyes, Bell, flacid_cheeks, cannot_wrinkle_forehead, and paresis_superficial_neck_musculature

For y we have hyperacuasis, dry_mouth and taste_loss_anterior_part_tongue

T

∪ { Lesion(stapedius_nerve)} ∪ {⌐ Sign( x )} ∪ {⌐ Complaint( y ) } ⊢ □

Slide34

Causal Reasoning Reasoning about cause – effect relationships is called causal reasoning.

The representation of causal knowledge in logic may be represented by means of collection of logical implications of the form :

cause

effect

Causal Reasoning

Slide35

Cause and effect are the conjunction of literals. They represent state of some parameter. Eg

. Level of a substance in blood. It may be qualitative or numeric conc(blood, sodium) = 125 conc(blood, sodium) = decreased

Eg

.

o

f causal reasoning

: Negative Feedback Process

Slide36

Negative Feedback Process

S

r

1

r

1

r

2

r

n-1

r

n

.

.

.

r

n

~s

Where s, r

i

, r

i

’ , 1≤i≤n, n≥1 are literals

Slide37

Image taken from: Peter Lucas, The Representation of Medical Reasoning Models in Resolution-based Theorem

Provers, Artificial Intelligence

Slide38

Example taken from: Peter

Lucas, The Representation of Medical Reasoning Models in Resolution-based Theorem

Provers

, Artificial Intelligence

Slide39

Example taken from: Peter

Lucas, The Representation of Medical Reasoning Models in Resolution-based Theorem

Provers

, Artificial Intelligence

Slide40

Example taken from: Peter

Lucas, The Representation of Medical Reasoning Models in Resolution-based Theorem

Provers

, Artificial Intelligence

Slide41

Logic Implication

Example taken from: Peter

Lucas, The Representation of Medical Reasoning Models in Resolution-based Theorem

Provers

, Artificial Intelligence

Slide42

Now how will negative feedback used in theorem prover?

The numeric or qualitative state of a substance is change. Theorem prover tries to match with predicate of the form cause -> effect.

Slide43

Accordingly effect of cause is found, now it will try to find effect generated due to this effect and so on.

Now in the example taken here it will end up proving a contradiction. Hence the effect due to the initial cause is nullified.

Slide44

We investigated the applicability of logic as a language for the representation of a number of medical reasoning models.It was shown that the language of first-order predicate logic

allowed for the precise, and compact, representation of these models.Generally, in translating domain knowledge into logic, many of the subtleties that can be expressed in natural language are lost. In our study, it appeared that this problem was less prominently present.

Conclusion

Slide45

References

[1] Peter

Lucas,

The

Representation of Medical Reasoning Models in Resolution-based Theorem

Provers

, Artificial

Intelligence

, Published in:

Artificial Intelligence in Medicine, 5(5),

395{414}, 1993

.

[2]

M. H. VAN EMDEN AND R. A.

KOWALSKI,

University of Edinburgh, Edinburgh,

Scotland, The

Semantics of Predicate Logic as a Programming

Language,

Journal of the

Association

for Computing Machinery,

Vol

23, No 4

,

pp

733-742,

October 1976

.

[3]

Artificial Intelligence in Medicine, Randall Davis,

Casimir A.

Kulikowski, Edited by Peter

Szolovits, AAAS Selected Symposia Series, Volume 51

,

1982 .

[4]

P.J.F. Lucas, R.W.

Segaar

, A.R.

Janssens

, HEPAR: an expert system for the

diagnosis of

disorders of the liver and biliary tract,

published in the journal of the international association for the study of the liver, Liver

9 (1989) 266-275

.

Slide46

Questions ?

Slide47

Thank You