EXPERT%20SYSTEMS

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EXPERT SYSTEMS

• Summary of Expert Systems in the Nursing Domain

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References

• 1. Darlington K. Basic expert systems.  ITIN
  1996 8.49-11. 2. Edwards JS.  Building
  Knowledge Based Systems.  Pitman Press, 1991. 3.
  Giarratano JC, Riley GD. Expert Systems
  Principles and Programming. 2n' edition.  PWS
  Kent, 1992. 4. Hayes-Roth F, Waterman DA, Lenat
  DB. Building Expert Systems.  Reading,
  Massachusetts Addison-Wesley, 1983. 5. Firlej
  M, HeUens D. Knowledge Elicitation-A practical
  handbook.  Prentice Hall 1992. 6. Hart A.
  Knowledge Acquisition for Expert Systems.  Kogan
  Page 1989. 7. Koeh W. Expert Nurse an expert
  system to reach nursing diagnosis.  ITCH
  conference proceedings 1996. 8. Roth K,
  DiStefano JI, Chang BL.  CANDI Development of
  the automated nursing assessment tool.  Computers
  in Nursing 1989 7.5222-7. 9. RodewaldLE. BABY
  An expert system for patient monitoring in a
  newborn intensive care unit.  MS Thesis,
  University of Illinois, Champaign - Urbana, 1984.
• http//www.bcsnsg.org.uk/itin09/darling.htm

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Introduction

• This summary on the nursing domain, although
  clearly stated by the author, Mr. Keith
  Darlington, characterizes further the concepts of
  expert systems rather than proving the
  application of the concepts in helping the
  domain, in my opinion. Nevertheless, some rare
  possibilities surfaced in an effort by the author
  to prove the relationship and effectiveness of
  expert systems and the nursing sphere. Perhaps,
  with all rights due to him, Mr. Darlington
  intentionally and successfully outlined the basic
  concepts of expert systems.

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Basic Architecture of an Expert System
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Inference Engine

• Inference Engine is an established set of rules
  to be tested based on certain conditions. If a
  certain condition is true after a series of
  questions, then a specific result is set to be
  triggered else, do otherwise.

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Sample Rules

• RULE 1
• IF room is cool
•     and light is poor
•         then best plant is ivy
• RULE 2 IF temperature lt 55         then room is
  cool.
• If the inference engine was trying to prove the
  conclusion in RULE 1, then it would require
  values for the two conditions in this rule.  That
  is, "room is cool" and "light is poor".

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The User Interface

• The user communicates with the system via the
  user interface that can be manipulated with the
  use of a mouse, the keyboard, light pen,
  touch-sensitive screen, and voice input. In
  addition to helping the nurse in answering
  questions through the user interface, the expert
  system will provide mechanisms for the nurse to
  ask questions as well.

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Uncertainty

• Because patients who are diagnosed with the same
  sickness may experience different level of
  comforts or pain, it is good practice to
  incorporate an uncertainty degree that will
  provide more flexibility to the nurse in
  providing answers to the system.

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Certainty Factors

• Thus, by generating certainty factors of 0 to
  100, where 50 is the median, is a good mechanism
  to measure certainty levels where as the 0 mark
  indicates no certainty, and the 100 mark
  indicates a high degree of certainty.

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Certainty Factors (Example)

• IF the patient diet is low in fat     AND the
  patient takes regular exercise         THEN
  the patient is healthy
• This may be true most of the time and have a high
  certainty factor as a result.

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Knowledge Engineering

• While such backward chaining mechanism can be
  useful not only in the nursing domain, it is also
  a known fact that it thrived in other areas that
  use the same heuristic approach. To determine,
  however, whether a system will succeed or not,
  the telephone test is a great tool. That is if a
  human expert can solve a problem over the
  telephone, equally, the system will too.
  Otherwise, the system will most likely fail. As
  a result, it is judicious for a computer
  scientist, or any team of computer scientists to
  employ knowledge engineering prior the
  manufacture of any expert system. The
  aforementioned technique is accomplished in two
  steps. One has to acquire the relevant data and
  enter them in the knowledge base for which the
  system is being fabricated.

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Knowledge Acquisition

• Obtaining the relevant data, yet, requires
  knowledge acquisition which is attainable through
  research and interviewing the experts in the
  field.

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Interviewing

• there are three knowledge sources in the nursing
  field. The clinical and literature data which
  can mainly be obtained through research, and the
  expert data which can be obtained by the most
  famously used knowledge acquisition which is
  interviewing. However, it may quiet difficult to
  gather the needed information from the nursing
  experts themselves because they me be either
  unmotivated, or not having the time it requires
  for them to be interviewed.

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Expert Systems Tools

• Once the information needed is assembled, the
  computer scientist(s) need to figure out what
  kind of tools will be needed to build the system.
  Programming languages such as C and Pascal, and
  AI languages such as LISP and Prolog are among
  the diverse languages used to build expert
  systems. Nevertheless, currently, expert
  systems shells are available for both computer
  scientists and non-computer scientists like
  skilled nurses as apparatus to create expert
  systems. The shells are liquidated expert
  systems from their knowledge base. They are
  non-flexible, but useful.

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Knowledge Acquisition Tools

• The shells come equipped with knowledge
  acquisition tools. This is done by an induction
  engine that reads a set of rules and tries to
  generate rules that are secured to the domain in
  question.

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Some Expert System Rules

• If an old man is a smoker, then he is a high risk
  patient.
• If a middle man is a non-smoker, then he is a low
  risk patient.
• If a young woman is a non-smoker, then she is a
  low risk patient.

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Other Expert Systems in the Field

• Expert Nurse
• CANDI (Computer Aided Nursing Diagnosis and
  Intervention)
• BABY that monitors ICU(Intensive Care Unit)
  babies