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EXPERT SYSTEMS

EXPERT SYSTEMS. Summary of Expert Systems in the Nursing Domain. References.

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EXPERT SYSTEMS

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  1. EXPERT SYSTEMS Summary of Expert Systems in the Nursing Domain

  2. References • 1. Darlington K. Basic expert systems.  ITIN 1996; 8.4:9-11. 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; 7.5:222-7. 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

  3. 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.

  4. Basic Architecture of an Expert System

  5. 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.

  6. Sample Rules • RULE 1 • IF room is cool •     and light is poor •         then best plant is ivy; • RULE 2 IF temperature < 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".”

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

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

  13. 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.

  14. 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 system’s 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.

  15. 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.

  16. 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.

  17. Other Expert Systems in the Field • Expert Nurse • CANDI (Computer Aided Nursing Diagnosis and Intervention) • BABY that monitors ICU(Intensive Care Unit) babies

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