Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: MYCIN was the one of the initial expert systems to perform with the level of expertise of a human expert and to provide users with complete explanation of its logical reasoning. Moreover, the techniques which were developed for MYCIN expert system have become most widely available design in the various small expert system building tools.
|Published (Last):||1 October 2009|
|PDF File Size:||13.75 Mb|
|ePub File Size:||3.33 Mb|
|Price:||Free* [*Free Regsitration Required]|
The system also known as an" expert system" would ask a series of questions designed to emulate the thinking of an expert in the field of infectious disease hence the "expert-" , and from the responses to these questions give a list of possible diagnoses, with probability, as well as recommend treatment hence the "decision support-". MYCIN has three sub-systems:. Consultation system Explanation System Rule Acquisition system 1.
Mycins Consultation System - works out possible organisms and suggests treatments Rule base. Rules stored as premise -action pairs Premises are conjunctions and disjunctions of conditions Conditions normally evaluate to True or False, with some certainty factor on the evidence. Rules also have certainty factors. Combined to form new certainty factors.
Certainty factor - degree of belief attached to information Actions are either conclusions e. It uses a backward chaining mechanism, reasoning back from the goals it want to prove to the data it has, rather than vice versa.
The overall goal is "compile a list of therapies. Questions can be prompted by the invocation of rules, to find out necessary data, to avoid unnecessary questions. It does this by manipulating its record of the rules it invoked, the goal it was trying to achieve, the information it was trying to discover. Can also answer general questions e. The Rule Acquisition System Experts can enter new rules or edit existing rules. The system automatically adds the new rule to.
Written in Lisp, a language a set of languages, actually geared towards artificial intelligence, MYCIN was one of the pioneering expert systems, and was the first such system implemented for the medical field. The case histories of ten patients with different types of meningitis were submitted to MYCIN as well as to eight human physicians, including a resident, a research fellow, and five faculty specialists in infectious disease. Both MYCIN's and the human physician's recommendations as well as a record of the treatment actually received by the patients were sent to eight non-Stanford specialists, completely unidentified as to which recommendation was MYCIN's and which were authored by the physicians.
The outside specialists gave MYCIN the highest score as far as accuracy of diagnosis and effectiveness of treatment. A knowledge base which stores the information the expert system "knows", much of which is derived from other information in the knowledge base.
An inference engine to derive knowledge from the presently known knowledge in the knowledge base. Humans interface with MYCIN by answering a series of diagnostic questions akin to what a physician may ask a patient, as well as prompting for relevant test results. MYCIN takes this data. The science of the generation of these rules is known as "knowledge engineering". MYCIN uses a modification of the method of reasoning called "backward chaining" to search its knowledge base.
In some cases this can be affected merely by changing the knowledge base. This wasn't because of any weakness in its 4. As mentioned, in tests it outperformed members of the Stanford medical school faculty.
Some observers raised ethical and legal issues related to the use of computers in medicine if a program gives the wrong diagnosis or recommends the wrong therapy, who should be held responsible? However, the greatest problem, and the reason that MYCIN was not used in routine practice, was the state of technologies for system integration, especially at the time it was developed.
The program ran on a large time-shared system, available over the early Internet ARPANet , before personal computers were developed. In the modern era, such a system would be integrated with medical record systems, would extract answers to questions from patient databases, and would be much less dependent on physician entry of information. In the s, a session with MYCIN could easily consume 30 minutes or morean unrealistic time commitment for a busy clinician.
MYCIN's greatest influence was accordingly its demonstration of the power of its representation and reasoning approach. Rule-based systems in many non-medical domains were developed in the years that followed MYCIN's introduction of the approach. A difficulty that rose to prominence during the development of MYCIN and subsequent complex expert systems has been the extraction of the necessary knowledge for the inference engine to use from the human expert in the relevant fields into the rule base the so-called knowledge engineering.
Learn more about Scribd Membership Home. Much more than documents. Discover everything Scribd has to offer, including books and audiobooks from major publishers. Start Free Trial Cancel anytime. Case Study of Mycin. Uploaded by Manmit Kaur.
Document Information click to expand document information Date uploaded Apr 13, Did you find this document useful? Is this content inappropriate? Report this Document. Flag for Inappropriate Content. Download Now.
Related titles. Carousel Previous Carousel Next. Jump to Page. Search inside document. Mycins Consultation System - works out possible organisms and suggests treatments Rule base Rules stored as premise -action pairs Premises are conjunctions and disjunctions of conditions Conditions normally evaluate to True or False, with some certainty factor on the evidence. Combined to form new certainty factors Certainty factor - degree of belief attached to information Actions are either conclusions e.
Rildo Reis Tan. Ashwani Kumar. Garima Singh. Haryuna Mohd Taharim. Tanya Bhanot. Victoria Offoma. Amulya Srivastava. Kiki Fadilah. Bin Zhang. Inassani Alifia. Vikas Kumar.
Benjamin Kiprono Blessed. Sathya Narayanan. Abhilasha Choyal. Vasundhara Srinivas. Ranbir Singh. Anonymous vQrJlEN. Janith Dassanayake. Popular in Technology. Andi Ihsan. Acyl Chloride Hariprem. Mohammod Ahad Arian. Greg Szklarz. Todd Dodge. James Johnson. Himanshu Negi. Sha Hussain. Macmillan Publishers. Gong Do.
Ambar Ans Brandedstore. Vijay Vijitizer. Software Requirements Specification Template.
Study and Analysis of MYCIN expert system
Case Study of Mycin
MYCIN was an early backward chaining expert system that used artificial intelligence to identify bacteria causing severe infections, such as bacteremia and meningitis , and to recommend antibiotics , with the dosage adjusted for patient's body weight — the name derived from the antibiotics themselves, as many antibiotics have the suffix "-mycin". The Mycin system was also used for the diagnosis of blood clotting diseases. It was written in Lisp as the doctoral dissertation of Edward Shortliffe under the direction of Bruce G. Buchanan, Stanley N. Cohen and others. At the end, it provided a list of possible culprit bacteria ranked from high to low based on the probability of each diagnosis, its confidence in each diagnosis' probability, the reasoning behind each diagnosis that is, MYCIN would also list the questions and rules which led it to rank a diagnosis a particular way , and its recommended course of drug treatment.
The system also known as an" expert system" would ask a series of questions designed to emulate the thinking of an expert in the field of infectious disease hence the "expert-" , and from the responses to these questions give a list of possible diagnoses, with probability, as well as recommend treatment hence the "decision support-". MYCIN has three sub-systems:. Consultation system Explanation System Rule Acquisition system 1. Mycins Consultation System - works out possible organisms and suggests treatments Rule base.
MYCIN would attempt to diagnose patients based on reported symptoms and medical test results. The program could request further information concerning the patient, as well as suggest additional laboratory tests, to arrive at a probable diagnosis , after which it would recommend a course of treatment. Using about production rules, MYCIN operated at roughly the same level of competence as human specialists in blood infections and rather better than general practitioners. Info Print Cite. Submit Feedback. Thank you for your feedback. Written By: B.