Expert Systems vs Clinical Decision Support Systems

Adil Alpkoçak
Adil AlpkoçakAssoc. Professor en Dokuz Eylül University
Expert system &
Clinical Decision
Support Systems
Zaporozhye State Medical University
Department of Medical and Pharmaceptical Informatics
2014
 Alexej An. Ryzhov
2
Artificial Intelligence in Medicine (AIM)
From the very earliest moments in the modern history of the computer,
scientists have dreamed of creating an ‘electronic brain’. Of all the modern
technological quests, this search to create artificially intelligent (AI) computer
systems has been one of the most ambitious and, not surprisingly,
controversial.
Medical artificial intelligence (AIM) is primarily concerned with
the construction of AI programs that perform diagnosis and make
therapy recommendations.
Unlike medical applications based on other programming
methods, such as purely statistical and probabilistic methods,
medical AI programs are based on symbolic models of disease
entities and their relationship to patient factors and clinical
manifestations.
3
Clinical Decision Support Systems (CDSS)
Clinical (or Diagnostic) Decision Support
Systems (CDSS) are interactive computer
programs, which directly assist physicians and
other health professionals with decision
making tasks1980s.
For medical diagnosis, there are scopes for
ambiguities in inputs, such as history, and
laboratory tests.
4
Types of CDSS
(Clinical Decision Support System)
 Architecture
 Stand alone program
 Decision support component
 Target domain
 Large Scale
 Focused CDSS
 Target users
 physicians
 Non-physicians (nurses, patients, other)
5
Purpose of systems
 Hospital information systems
 Only electronic patient record information management.
 Notification Systems
 Specific reminders at particular clinical situations.
 Acute Care Systems
 Help to assess faster all the parameters when a quick estimation and decision has to be
made.
 Laboratory Systems
 Support the work with ordering laboratory tests and assessing the results
 Drug therapy systems
 Support drug choosing, dosing, preventing adverse drug effects. Reviewing latest information
on drugs.
 Quality Assurance and Administration Systems
 Support the heath care management at the hospital level. Focusing on the whole health care
system rather then on a particular patient. Cost analysis.
 Educational Systems
 Intended for the use by medical students or young doctors in education.
 Research Systems
 Clinical Trials and other medical research support
6
The CDSSs can be used at several
stages of treating of a patient:
 establishing the correct diagnosis for the patient
coming with certain complains
 choosing the best therapeutic strategy according to
the situation and patient’s preferences
 monitoring the therapy
 assisting at the choosing the best drug from a
specified drug-group, drug dosing and observing the
possible drug-drug interactions
 preventive medical examinations and tests
 browsing the knowledge base of the CDSS
7
Expert system
An expert system is a class of computer
programs developed by researchers in artificial
intelligence during the 1970s and applied
commercially throughout the 1980s.
Expert systems are computerized tools
designed to enhance the quality and
availability of knowledge required by decision
makers in a wide range of industries.
8
Expert system
Types of problems solved by expert
systems
Typically, the problems to be solved are of
the sort that would normally be tackled by a
human "expert“ a medical or other
professional, in most cases.
Generally expert systems are used for problems for which
there is no single "correct" solution which can be encoded in
a conventional algorithm” one would not write an expert
system to find shortest paths through graphs, or sort data,
as there are simply easier ways to do these tasks.
Simple systems use simple true/false logic to evaluate data,
but more sophisticated systems are capable of performing
at least some evaluation taking into account real-world
uncertainties, using such methods as fuzzy logic. Such
sophistication is difficult to develop and still highly imperfect.
E-commerce
Decision Support
Business
Engineering
Military
Marketing/Sales
Agriculture
Medical
Web Design
Human Resources
Computer Sciences
Legal
Science
Construction
Transportation
Research
&Development
Environmental
9
Expert system
− Specifically, the goals of developing expert systems for medicine
are as follows
− to improve the accuracy of clinical diagnosis through approaches that are
systematic, complete, and able to integrate data from diverse sources;
− to improve the reliability of clinical decisions by avoiding unwarranted
influences of similar but not identical cases ;
− to improve the cost efficiency of tests and therapies by balancing the expenses
of time, inconvenience against benefits, and risks of definitive actions ;
− to improve our understanding of the structure of medical knowledge, with the
associated development of techniques for identifying inconsistencies and
inadequacies in that knowledge ;
− to improve our understanding of clinical decision-making, in order to improve
medical teaching and to make the system more effective and easier to
understand.
10
Expert system
Mycin
The system was designed to diagnose infectious blood
diseases and recommend antibiotics, with the dosage
adjusted for patient's body weight the name derived from
the antibiotics themselves, as many have the suffix "-
mycin".
Mycin operated using a fairly simple inference engine, and a
knowledge base of ~500 rules. It worked by querying the
physician through a long series of simple yes/no or textual
questions, at the end of which, it provided a list of possible culprit
bacteria, its confidence in each diagnosis, the reasoning
(referring to individual questions and answers) behind each
diagnosis, and its recommended course of drug treatment.
11
Expert system
CADUCEUS
CADUCEUS was a medical expert system
developed in the mid-1980s. Their motivation
was an intent to improve on MYCIN - which
focussed on blood-borne infectious bacteria -
to focus on more comprehensive issues than a
narrow field like blood poisoning; instead
embracing all internal medicine. CADUCEUS
eventually could diagnose ~1000 diseases.
Expert laboratory information systems
A Laboratory Information Management System (LIMS), sometimes
referred to as a Laboratory Information System (LIS) or Laboratory
Management System (LMS), is a software-based laboratory and
information management system that offers a set of key features that
support a modern laboratory's operations.
Laboratory expert systems usually do not intrude into clinical practice.
This systems embedded within the process of care, and with the
exception of laboratory staff, clinicians working with patients do not
need to interact with them. For the ordering clinician, the system prints
a report with a diagnostic hypothesis for consideration, but does not
remove responsibility for information gathering, examination,
assessment and treatment. For the pathologist, the system cuts down
the workload of generating reports, without removing the need to check
and correct them.
12
Expert laboratory information systems
Pathology Expert Interpretative Reporting System (PEIRS)
A more general example of Expert laboratory information
systems is Pathology Expert Interpretative Reporting System.
During its period of operation, PEIRS interpreted about 80–100
laboratory reports a day with a diagnostic accuracy of about
95%. It accounted for about 20% of all the reports generated by
the hospital’s chemical pathology department. PEIRS reported
on thyroid function tests, arterial blood gases, urine and plasma
catecholamines, hCG (human chorionic gonadotrophin) and
alfafetoprotein (AFP ), glucose tolerance tests, cortisol, gastrin,
cholinesterase phenotypes and parathyroid hormone-related
peptide (PTH-RP).
13
User Interface
USER
Inference Engine
Inference + Control
Knowledge base:
”Rules” + “Facts”
Knowledge acquisition
Subsystem
KNOWLEDGE
ENGINEER
Explanation Facility Working Memory
14
Expert system
Expert systems differ from
conventional applications
software in the following
ways:
• The expert system shell,
or interpreter.
• The existence of a
"knowledge base," or
system of related
concepts that enable the
computer to approximate
human judgment.
• The sophistication of the
user interface.
15
Expert system
Individuals involved with expert systems
There are generally three individuals having an
interaction with expert systems.
Primary among these is the end-user.
In the building and maintenance of the system
there are two other roles:
• the problem domain expert
• knowledge engineer
16
Expert system
The end user
The end-user usually sees an expert system through an interactive
dialog.
As can be seen from this dialog, the system is leading the user
through a set of questions, the purpose of which is to determine a
suitable set of restaurants to recommend. In expert systems,
dialogs are not pre-planned. There is no fixed control structure.
Dialogs are synthesized from the current information and the
contents of the knowledge base. Because of this, not being able to
supply the answer to a particular questions does not stop the
consultation. In expert systems, dialogs are not pre-planned. There
is no fixed control structure. Dialogs are synthesized from the
current information and the contents of the knowledge base.
Because of this, not being able to supply the answer to a particular
questions does not stop the consultation.
17
Expert system
The knowledge engineer
Knowledge engineers are concerned with the
representation chosen for the expert's
knowledge declarations and with the inference
engine used to process that knowledge.
18
Expert system
The knowledge engineer
Knowledge engineers are concerned with the representation chosen for
the expert's knowledge declarations and with the inference engine used to
process that knowledge. There are several characteristics known to be
appropriate to a good inference technique.
1. A good inference technique is independent of the problem domain.
In order to realize the benefits of explanation, knowledge transparency,
and reusability of the programs in a new problem domain, the inference
engine must not contain domain specific expertise.
2. Inference techniques may be specific to a particular task, such as
diagnosis of hardware configuration. Other techniques may be committed
only to a particular processing technique.
3. Inference techniques are always specific to the knowledge structures.
4. Successful examples of rule processing techniques include:
(a) Forward chaining
(b) Backward chaining
Rule-based expert systems
In an expert system, the
knowledge is usually
represented as a set of rules.
The reasoning method is usually
either logical or probabilistic. An
expert system consists of three
basic components :
● a knowledge base, which
contains the rules necessary for
the completion of its task;
● a working memory in which
data and conclusions can be
stored;
● an inference engine which
matches rules to data to derive
its conclusions.
19
Rule-based expert systems
For a task like interpreting an ECG, an example of a rule that could be used to
detect asystole might be:
RuleASY1:
If heart rate 0
then conclude asystole
If the expert system was attached to a patient monitor then a second rule whose
role was to filter out false asystole alarms in the presence of a normal arterial
waveform might be:
Rule ASY2:
If asystole
and (ABP is pulsatile and in the normal range)
then retract asystole
In the presence of a zero heart rate, the expert system would first match rule ASY1 and
conclude that asystole was present. However, if it next succeeded in matching all the
conditions in rule ASY2, then it would fire this second rule, which would effectively filter
out the previous asystole alarm. If rule ASY2 could not be fired because the arterial
pressure was abnormal, then the initial conclusion that asystole was present would
remain. 20
21
Expert system
The Inference Rule
An understanding of the "inference rule"
concept is important to understand expert
systems. An inference rule is a statement that
has two parts, an if-clause and a then-clause.
IF
It is raining
THEN
You should wear a raincoat
22
Expert system
The Inference Rule
With Exsys CORVID, these rules are very similar to the
form that you would use to explain the heuristic using
English and algebra. For example, “If the investment
customer has a high risk tolerance and requires rapid
growth to reach their objectives, Mutual Fund X would
be a good choice.” In a rule this would become:
IF
The customer has high-risk tolerance
AND
Meeting objectives requires rapid growth
THEN
Mutual Fund X is a good choice
This rule includes a small amount of syntax, but it is still very
easy to read and understand what it means. If you built similar
rules for each of the heuristics in the decision-making process,
you would have the logic for the expert system.
23
Expert system
Arden syntax
The Arden syntax supports the generation of
rules for alerts or reminders.
Arden syntax - A standard language for writing
situation-action rules that can trigger alerts based
on abnormal clinical events detected by a clinical
information system.
24
Expert system
Arden syntax
To detect a low potassium level and to identify
thiazides as a possible cause, the following MLM is
created:
DATA: POTAS-STORAGE := event {serum potassium}
POTAS := LAST {serum potassium}
THIAZIDE-US E := {current prescription for thiazides}
EVOKE:
POTAS-STORAGE
LOGIC:
IF POTAS < 3 THEN CONCLUDE TRUE
ELSE CONCLUDE FALSE
ACTION:
SEND "Patient is hypokalemic. This condition could be caused by
thiazides."
25
Expert system
Arden syntax
This MLM will be executed each time that a serum potassium level is
stored in the database (the EVOKE slot). The patient data required are
the last serum potassium value and whether the patient uses thiazide
diuretics (the DATA slot). If the last potassium level is less than 3 (the
LOGIC slot), an alert is sent to the clinician (the ACTION slot). The
following statements specify that the potassium level must be measured
when treatment with a thiazide is initiated:
DATA:
THIAZIDE-START := event {start of prescription for thiazides}
POTAS := LAST {serum potassium}
EVOKE:
THIAZIDE-START
LOGIC:
IF POTAS OCCURRED WITHIN 2 MONTHS PRECEDING NOW THEN CONCLUDE FALSE
ELSE CONCLUDE TRUE
ACTION:
SEND "When starting a treatment with thiazides, obtain a baseline measurement of the potassium
level."
This MLM will be executed each time a patient is started on thiazide diuretics (the EVOKE
slot). The patient data required involve the last serum potassium value (the DATA slot). If the
last potassium value is older than 2 months (the LOGIC slot), an alert is sent to the clinician
(the ACTION slot).
26
Expert system
THE EXPERT SYSTEM SHELL
An expert system shell provides a layer
between the user interface and computer
operating system to manage the input and
output of data. It also manipulates the
information provided by the user in
conjunction with the knowledge base to arrive
at a particular conclusion.
27
Expert system
THE USER INTERFACE
For the last several years, interface designs for expert systems
have hinged on graphical capabilities and unconventional
methods of entering data into the system. Graphical
interfaces can supply information in any number of
forms: simple text "dressed up" in windows, pop-up
menus, or actual graphical objects.
Recently, many of those formats have been integrated into
conventional applications, but they are of particular use in expert
systems. An expert system may express an idea, solution, or
explanation using more complex conventions than rows of
numbers, pie charts, or brief messages.
28
Expert system
THE KNOWLEDGE BASE
The main purpose of the knowledge base is to
provide the guts of the expert system--the
connections between ideas, concepts, and
statistical probabilities that allow the reasoning
part of the system to perform an accurate
evaluation of a potential problem.
29
Expert system
Prolog
Prolog is a logic programming language.
Prolog is used in many artificial intelligence
programs and in computational linguistics
Prolog is based on first-order predicate
calculus, however it is restricted to allow only
Horn clauses.
30
Expert system
Prolog
Prolog Programming in Prolog is very different from programming
in a procedural language. In Prolog you supply a database of facts
and rules; you can then perform queries on the database. The
basic unit of Prolog is the predicate, which is defined to be true. A
predicate consists of a head and a number of arguments. For
example:
cat(tom).
This enters into the database the fact that 'tom' is a 'cat'. More formally, 'cat' is
the head, and 'tom' is the single argument. Here are some sample queries you
could ask a Prolog interpreter basing on this fact:
is tom a cat?
?- cat(tom).
yes.
what things are cats?
?- cat(X).
X = tom;
yes
31
Expert system
EON/Protege
• EON is a new architecture for second generation
component based Clinical Decision Support
Systems developed at Stanford University
• Protege is a set of software tools (developed by the
same group) for building components for a CDSS
• Therapy Helper (AIDS), Breast Cancer, Hypertension
 http://protege.stanford.edu
32
Expert system
R1
The R1 program was a production-rule-based
system written in OPS5 by John P. McDermott
of CMU in 1978 to assist in the ordering of
DEC's VAX computer systems by automatically
selecting the computer system components
based on the customer's requirements.
Symptoms KB
Disease KB
Existing clin. guidelines
Traditional
Examination
physician patient
EPR
CDSS
CONSULTING ROOM
New large-scale CDSS
(internal medicine)
Sypmtoms updates
Terminology updates
Disease updates
Guidelines updates
a) b)
Internist
Ophtalmologist
Orthopaedist
GP
data
data
data
data
decision
support
Physician-patient-CDSS
consulting mode
a) b)
previous KB system 1
previous KB system 2
Existing
established
terminology
systems
(MeSH, UMLS,
ICD10, ATC)
Focused CDSS 2
Focused CDSS 1
new
EPR
standards
Medline and similar systems
manual
dataentry
… various specialities
HOSPITAL
HL7
GLIF3
EPR
-patient history
-lab. results
-prev. therapies
data
data
API
API
medical
research
Knowledge base
User Interface
Inference
Engine
Update Interface
33
Decision support systems need not be ‘stand
alone’ but can be deeply integrated into an
electronic medical record system. Indeed, such
integration reduces the barriers to using such
a system, by crafting them more closely into clinical
working processes, rather than expecting workers
to create new processes to use them.
The HELP system is an example of this type of
knowledge-based hospital information system. It
not only supports the routine applications of a
hospital information system including management
of admissions and discharges and order-entry, but
also provides a decision support function. The
decision support system has been actively
incorporated into the functions of the routine HIS
applications. Decision support provides clinicians
with alerts and reminders, data interpretation and
patient diagnosis facilities, patient management
suggestions and clinical protocols. Activation of the
decision support is provided within the applications
but can also be triggered automatically as clinical
data are entered into the patient’s computerized
record.
Integration
Decision Support Systems to
Hospital Information System
34
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Expert Systems vs Clinical Decision Support Systems

  • 1. Expert system & Clinical Decision Support Systems Zaporozhye State Medical University Department of Medical and Pharmaceptical Informatics 2014  Alexej An. Ryzhov
  • 2. 2 Artificial Intelligence in Medicine (AIM) From the very earliest moments in the modern history of the computer, scientists have dreamed of creating an ‘electronic brain’. Of all the modern technological quests, this search to create artificially intelligent (AI) computer systems has been one of the most ambitious and, not surprisingly, controversial. Medical artificial intelligence (AIM) is primarily concerned with the construction of AI programs that perform diagnosis and make therapy recommendations. Unlike medical applications based on other programming methods, such as purely statistical and probabilistic methods, medical AI programs are based on symbolic models of disease entities and their relationship to patient factors and clinical manifestations.
  • 3. 3 Clinical Decision Support Systems (CDSS) Clinical (or Diagnostic) Decision Support Systems (CDSS) are interactive computer programs, which directly assist physicians and other health professionals with decision making tasks1980s. For medical diagnosis, there are scopes for ambiguities in inputs, such as history, and laboratory tests.
  • 4. 4 Types of CDSS (Clinical Decision Support System)  Architecture  Stand alone program  Decision support component  Target domain  Large Scale  Focused CDSS  Target users  physicians  Non-physicians (nurses, patients, other)
  • 5. 5 Purpose of systems  Hospital information systems  Only electronic patient record information management.  Notification Systems  Specific reminders at particular clinical situations.  Acute Care Systems  Help to assess faster all the parameters when a quick estimation and decision has to be made.  Laboratory Systems  Support the work with ordering laboratory tests and assessing the results  Drug therapy systems  Support drug choosing, dosing, preventing adverse drug effects. Reviewing latest information on drugs.  Quality Assurance and Administration Systems  Support the heath care management at the hospital level. Focusing on the whole health care system rather then on a particular patient. Cost analysis.  Educational Systems  Intended for the use by medical students or young doctors in education.  Research Systems  Clinical Trials and other medical research support
  • 6. 6 The CDSSs can be used at several stages of treating of a patient:  establishing the correct diagnosis for the patient coming with certain complains  choosing the best therapeutic strategy according to the situation and patient’s preferences  monitoring the therapy  assisting at the choosing the best drug from a specified drug-group, drug dosing and observing the possible drug-drug interactions  preventive medical examinations and tests  browsing the knowledge base of the CDSS
  • 7. 7 Expert system An expert system is a class of computer programs developed by researchers in artificial intelligence during the 1970s and applied commercially throughout the 1980s. Expert systems are computerized tools designed to enhance the quality and availability of knowledge required by decision makers in a wide range of industries.
  • 8. 8 Expert system Types of problems solved by expert systems Typically, the problems to be solved are of the sort that would normally be tackled by a human "expert“ a medical or other professional, in most cases. Generally expert systems are used for problems for which there is no single "correct" solution which can be encoded in a conventional algorithm” one would not write an expert system to find shortest paths through graphs, or sort data, as there are simply easier ways to do these tasks. Simple systems use simple true/false logic to evaluate data, but more sophisticated systems are capable of performing at least some evaluation taking into account real-world uncertainties, using such methods as fuzzy logic. Such sophistication is difficult to develop and still highly imperfect. E-commerce Decision Support Business Engineering Military Marketing/Sales Agriculture Medical Web Design Human Resources Computer Sciences Legal Science Construction Transportation Research &Development Environmental
  • 9. 9 Expert system − Specifically, the goals of developing expert systems for medicine are as follows − to improve the accuracy of clinical diagnosis through approaches that are systematic, complete, and able to integrate data from diverse sources; − to improve the reliability of clinical decisions by avoiding unwarranted influences of similar but not identical cases ; − to improve the cost efficiency of tests and therapies by balancing the expenses of time, inconvenience against benefits, and risks of definitive actions ; − to improve our understanding of the structure of medical knowledge, with the associated development of techniques for identifying inconsistencies and inadequacies in that knowledge ; − to improve our understanding of clinical decision-making, in order to improve medical teaching and to make the system more effective and easier to understand.
  • 10. 10 Expert system Mycin The system was designed to diagnose infectious blood diseases and recommend antibiotics, with the dosage adjusted for patient's body weight the name derived from the antibiotics themselves, as many have the suffix "- mycin". Mycin operated using a fairly simple inference engine, and a knowledge base of ~500 rules. It worked by querying the physician through a long series of simple yes/no or textual questions, at the end of which, it provided a list of possible culprit bacteria, its confidence in each diagnosis, the reasoning (referring to individual questions and answers) behind each diagnosis, and its recommended course of drug treatment.
  • 11. 11 Expert system CADUCEUS CADUCEUS was a medical expert system developed in the mid-1980s. Their motivation was an intent to improve on MYCIN - which focussed on blood-borne infectious bacteria - to focus on more comprehensive issues than a narrow field like blood poisoning; instead embracing all internal medicine. CADUCEUS eventually could diagnose ~1000 diseases.
  • 12. Expert laboratory information systems A Laboratory Information Management System (LIMS), sometimes referred to as a Laboratory Information System (LIS) or Laboratory Management System (LMS), is a software-based laboratory and information management system that offers a set of key features that support a modern laboratory's operations. Laboratory expert systems usually do not intrude into clinical practice. This systems embedded within the process of care, and with the exception of laboratory staff, clinicians working with patients do not need to interact with them. For the ordering clinician, the system prints a report with a diagnostic hypothesis for consideration, but does not remove responsibility for information gathering, examination, assessment and treatment. For the pathologist, the system cuts down the workload of generating reports, without removing the need to check and correct them. 12
  • 13. Expert laboratory information systems Pathology Expert Interpretative Reporting System (PEIRS) A more general example of Expert laboratory information systems is Pathology Expert Interpretative Reporting System. During its period of operation, PEIRS interpreted about 80–100 laboratory reports a day with a diagnostic accuracy of about 95%. It accounted for about 20% of all the reports generated by the hospital’s chemical pathology department. PEIRS reported on thyroid function tests, arterial blood gases, urine and plasma catecholamines, hCG (human chorionic gonadotrophin) and alfafetoprotein (AFP ), glucose tolerance tests, cortisol, gastrin, cholinesterase phenotypes and parathyroid hormone-related peptide (PTH-RP). 13
  • 14. User Interface USER Inference Engine Inference + Control Knowledge base: ”Rules” + “Facts” Knowledge acquisition Subsystem KNOWLEDGE ENGINEER Explanation Facility Working Memory 14 Expert system Expert systems differ from conventional applications software in the following ways: • The expert system shell, or interpreter. • The existence of a "knowledge base," or system of related concepts that enable the computer to approximate human judgment. • The sophistication of the user interface.
  • 15. 15 Expert system Individuals involved with expert systems There are generally three individuals having an interaction with expert systems. Primary among these is the end-user. In the building and maintenance of the system there are two other roles: • the problem domain expert • knowledge engineer
  • 16. 16 Expert system The end user The end-user usually sees an expert system through an interactive dialog. As can be seen from this dialog, the system is leading the user through a set of questions, the purpose of which is to determine a suitable set of restaurants to recommend. In expert systems, dialogs are not pre-planned. There is no fixed control structure. Dialogs are synthesized from the current information and the contents of the knowledge base. Because of this, not being able to supply the answer to a particular questions does not stop the consultation. In expert systems, dialogs are not pre-planned. There is no fixed control structure. Dialogs are synthesized from the current information and the contents of the knowledge base. Because of this, not being able to supply the answer to a particular questions does not stop the consultation.
  • 17. 17 Expert system The knowledge engineer Knowledge engineers are concerned with the representation chosen for the expert's knowledge declarations and with the inference engine used to process that knowledge.
  • 18. 18 Expert system The knowledge engineer Knowledge engineers are concerned with the representation chosen for the expert's knowledge declarations and with the inference engine used to process that knowledge. There are several characteristics known to be appropriate to a good inference technique. 1. A good inference technique is independent of the problem domain. In order to realize the benefits of explanation, knowledge transparency, and reusability of the programs in a new problem domain, the inference engine must not contain domain specific expertise. 2. Inference techniques may be specific to a particular task, such as diagnosis of hardware configuration. Other techniques may be committed only to a particular processing technique. 3. Inference techniques are always specific to the knowledge structures. 4. Successful examples of rule processing techniques include: (a) Forward chaining (b) Backward chaining
  • 19. Rule-based expert systems In an expert system, the knowledge is usually represented as a set of rules. The reasoning method is usually either logical or probabilistic. An expert system consists of three basic components : ● a knowledge base, which contains the rules necessary for the completion of its task; ● a working memory in which data and conclusions can be stored; ● an inference engine which matches rules to data to derive its conclusions. 19
  • 20. Rule-based expert systems For a task like interpreting an ECG, an example of a rule that could be used to detect asystole might be: RuleASY1: If heart rate 0 then conclude asystole If the expert system was attached to a patient monitor then a second rule whose role was to filter out false asystole alarms in the presence of a normal arterial waveform might be: Rule ASY2: If asystole and (ABP is pulsatile and in the normal range) then retract asystole In the presence of a zero heart rate, the expert system would first match rule ASY1 and conclude that asystole was present. However, if it next succeeded in matching all the conditions in rule ASY2, then it would fire this second rule, which would effectively filter out the previous asystole alarm. If rule ASY2 could not be fired because the arterial pressure was abnormal, then the initial conclusion that asystole was present would remain. 20
  • 21. 21 Expert system The Inference Rule An understanding of the "inference rule" concept is important to understand expert systems. An inference rule is a statement that has two parts, an if-clause and a then-clause. IF It is raining THEN You should wear a raincoat
  • 22. 22 Expert system The Inference Rule With Exsys CORVID, these rules are very similar to the form that you would use to explain the heuristic using English and algebra. For example, “If the investment customer has a high risk tolerance and requires rapid growth to reach their objectives, Mutual Fund X would be a good choice.” In a rule this would become: IF The customer has high-risk tolerance AND Meeting objectives requires rapid growth THEN Mutual Fund X is a good choice This rule includes a small amount of syntax, but it is still very easy to read and understand what it means. If you built similar rules for each of the heuristics in the decision-making process, you would have the logic for the expert system.
  • 23. 23 Expert system Arden syntax The Arden syntax supports the generation of rules for alerts or reminders. Arden syntax - A standard language for writing situation-action rules that can trigger alerts based on abnormal clinical events detected by a clinical information system.
  • 24. 24 Expert system Arden syntax To detect a low potassium level and to identify thiazides as a possible cause, the following MLM is created: DATA: POTAS-STORAGE := event {serum potassium} POTAS := LAST {serum potassium} THIAZIDE-US E := {current prescription for thiazides} EVOKE: POTAS-STORAGE LOGIC: IF POTAS < 3 THEN CONCLUDE TRUE ELSE CONCLUDE FALSE ACTION: SEND "Patient is hypokalemic. This condition could be caused by thiazides."
  • 25. 25 Expert system Arden syntax This MLM will be executed each time that a serum potassium level is stored in the database (the EVOKE slot). The patient data required are the last serum potassium value and whether the patient uses thiazide diuretics (the DATA slot). If the last potassium level is less than 3 (the LOGIC slot), an alert is sent to the clinician (the ACTION slot). The following statements specify that the potassium level must be measured when treatment with a thiazide is initiated: DATA: THIAZIDE-START := event {start of prescription for thiazides} POTAS := LAST {serum potassium} EVOKE: THIAZIDE-START LOGIC: IF POTAS OCCURRED WITHIN 2 MONTHS PRECEDING NOW THEN CONCLUDE FALSE ELSE CONCLUDE TRUE ACTION: SEND "When starting a treatment with thiazides, obtain a baseline measurement of the potassium level." This MLM will be executed each time a patient is started on thiazide diuretics (the EVOKE slot). The patient data required involve the last serum potassium value (the DATA slot). If the last potassium value is older than 2 months (the LOGIC slot), an alert is sent to the clinician (the ACTION slot).
  • 26. 26 Expert system THE EXPERT SYSTEM SHELL An expert system shell provides a layer between the user interface and computer operating system to manage the input and output of data. It also manipulates the information provided by the user in conjunction with the knowledge base to arrive at a particular conclusion.
  • 27. 27 Expert system THE USER INTERFACE For the last several years, interface designs for expert systems have hinged on graphical capabilities and unconventional methods of entering data into the system. Graphical interfaces can supply information in any number of forms: simple text "dressed up" in windows, pop-up menus, or actual graphical objects. Recently, many of those formats have been integrated into conventional applications, but they are of particular use in expert systems. An expert system may express an idea, solution, or explanation using more complex conventions than rows of numbers, pie charts, or brief messages.
  • 28. 28 Expert system THE KNOWLEDGE BASE The main purpose of the knowledge base is to provide the guts of the expert system--the connections between ideas, concepts, and statistical probabilities that allow the reasoning part of the system to perform an accurate evaluation of a potential problem.
  • 29. 29 Expert system Prolog Prolog is a logic programming language. Prolog is used in many artificial intelligence programs and in computational linguistics Prolog is based on first-order predicate calculus, however it is restricted to allow only Horn clauses.
  • 30. 30 Expert system Prolog Prolog Programming in Prolog is very different from programming in a procedural language. In Prolog you supply a database of facts and rules; you can then perform queries on the database. The basic unit of Prolog is the predicate, which is defined to be true. A predicate consists of a head and a number of arguments. For example: cat(tom). This enters into the database the fact that 'tom' is a 'cat'. More formally, 'cat' is the head, and 'tom' is the single argument. Here are some sample queries you could ask a Prolog interpreter basing on this fact: is tom a cat? ?- cat(tom). yes. what things are cats? ?- cat(X). X = tom; yes
  • 31. 31 Expert system EON/Protege • EON is a new architecture for second generation component based Clinical Decision Support Systems developed at Stanford University • Protege is a set of software tools (developed by the same group) for building components for a CDSS • Therapy Helper (AIDS), Breast Cancer, Hypertension  http://protege.stanford.edu
  • 32. 32 Expert system R1 The R1 program was a production-rule-based system written in OPS5 by John P. McDermott of CMU in 1978 to assist in the ordering of DEC's VAX computer systems by automatically selecting the computer system components based on the customer's requirements.
  • 33. Symptoms KB Disease KB Existing clin. guidelines Traditional Examination physician patient EPR CDSS CONSULTING ROOM New large-scale CDSS (internal medicine) Sypmtoms updates Terminology updates Disease updates Guidelines updates a) b) Internist Ophtalmologist Orthopaedist GP data data data data decision support Physician-patient-CDSS consulting mode a) b) previous KB system 1 previous KB system 2 Existing established terminology systems (MeSH, UMLS, ICD10, ATC) Focused CDSS 2 Focused CDSS 1 new EPR standards Medline and similar systems manual dataentry … various specialities HOSPITAL HL7 GLIF3 EPR -patient history -lab. results -prev. therapies data data API API medical research Knowledge base User Interface Inference Engine Update Interface 33 Decision support systems need not be ‘stand alone’ but can be deeply integrated into an electronic medical record system. Indeed, such integration reduces the barriers to using such a system, by crafting them more closely into clinical working processes, rather than expecting workers to create new processes to use them. The HELP system is an example of this type of knowledge-based hospital information system. It not only supports the routine applications of a hospital information system including management of admissions and discharges and order-entry, but also provides a decision support function. The decision support system has been actively incorporated into the functions of the routine HIS applications. Decision support provides clinicians with alerts and reminders, data interpretation and patient diagnosis facilities, patient management suggestions and clinical protocols. Activation of the decision support is provided within the applications but can also be triggered automatically as clinical data are entered into the patient’s computerized record. Integration Decision Support Systems to Hospital Information System
  • 34. 34