MEDUSA: A Fuzzy Expert System for Medical Diagnosis of Acute Abdominal Pain

Medical knowledge is expanding fast and it is difficult for general practitioners to remain abreast of all medical domains. Also, access to domain specialist is limited due to availability and geographical constraints. In many situations the diagnosis in [sic] upon the decision of the general practitioner and in cases this has resulted in the problem of patient's misdiagnosis. The purpose of this research is to create an expert system as a decision support model which is capable of risk analysis for diagnosis based on the patient's demography and laboratory tests. The expert system is designed in compliancy with medical communications protocol such as HL7 and can be integrated to any HL7 compliant Electronic Medical records system to provide more intelligence in diagnosis. Using linear scoring models and Fuzzy logic, the patient's demography and laboratory results will be used as rule bases. Such knowledge will be used as priors for a Bayesian engine to create the diagnostic spaces. Patient's information is compared in the space and the general practitioner can select between the possible hypotheses. Each diagnostic decision will be associated with a risk value. Using such scoring model provides a new semantic in diagnosis by providing risk values for every diagnosis made and by suggesting the most suitable treatment. Unlike many other existing expert systems, the architecture is designed in a generic standard which provides the capability to use the system for all medical domains. Achieving this generality has been a major goal achieved and its details are discussed in this document.

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Weights Optimization in a Rule-Based Expert System : An Application to the Diagnosis of Acute Abdominal Pain

Objective Medical Decision-Making Systems Approach in Disease - Lecture Notes in Medical Informatics ◽

10.1007/978-3-642-93308-0_5 ◽

1986 ◽

pp. 25-30 ◽

Cited By ~ 1

Author(s):

Jean-Louis Golmard ◽

◽

Michel Rodary

Keyword(s):

Abdominal Pain ◽

Expert System ◽

Acute Abdominal Pain ◽

Rule Based

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Decision support in acute abdominal pain using an expert system for different knowledge bases

Proceedings of Computer Based Medical Systems ◽

10.1109/cbms.1997.596400 ◽

2002 ◽

Cited By ~ 3

Author(s):

H.P. Eich ◽

C. Ohmann ◽

K. Lang

Keyword(s):

Abdominal Pain ◽

Decision Support ◽

Expert System ◽

Acute Abdominal Pain ◽

Knowledge Bases

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A generic expert system framework with fuzzy Bayesian inference for medical classification and diagnosis in cardialogy [i.e. cardiology

10.32920/ryerson.14655996 ◽

2021 ◽

Author(s):

Hossein Rahnama

Keyword(s):

General Practitioner ◽

Expert System ◽

Medical Knowledge ◽

Decision Support Model ◽

Diagnostic Decision ◽

Suitable Treatment ◽

Patient’S Information ◽

Communications Protocol ◽

Medical Records System ◽

Rule Bases

Medical knowledge is expanding fast and it is difficult for general practitioners to remain abreast of all medical domains. Also, access to domain specialist is limited due to availability and geographical constraints. In many situations the diagnosis in [sic] upon the decision of the general practitioner and in cases this has resulted in the problem of patient's misdiagnosis. The purpose of this research is to create an expert system as a decision support model which is capable of risk analysis for diagnosis based on the patient's demography and laboratory tests. The expert system is designed in compliancy with medical communications protocol such as HL7 and can be integrated to any HL7 compliant Electronic Medical records system to provide more intelligence in diagnosis. Using linear scoring models and Fuzzy logic, the patient's demography and laboratory results will be used as rule bases. Such knowledge will be used as priors for a Bayesian engine to create the diagnostic spaces. Patient's information is compared in the space and the general practitioner can select between the possible hypotheses. Each diagnostic decision will be associated with a risk value. Using such scoring model provides a new semantic in diagnosis by providing risk values for every diagnosis made and by suggesting the most suitable treatment. Unlike many other existing expert systems, the architecture is designed in a generic standard which provides the capability to use the system for all medical domains. Achieving this generality has been a major goal achieved and its details are discussed in this document.

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Bayesian Diagnostic Probabilities without Assuming Independence of Symptoms

Methods of Information in Medicine ◽

10.1055/s-0038-1634813 ◽

1991 ◽

Vol 30 (01) ◽

pp. 15-22 ◽

Cited By ~ 23

Author(s):

A. Gammerman ◽

A. R. Thatcher

Keyword(s):

Abdominal Pain ◽

Acute Abdominal Pain ◽

Final Diagnosis ◽

Bayes Theorem ◽

Bayes Method ◽

Hospital Records ◽

Computer Diagnosis ◽

Bayes Model ◽

Chi Squared ◽

Past Data

The paper describes an application of Bayes’ Theorem to the problem of estimating from past data the probabilities that patients have certain diseases, given their symptoms. The data consist of hospital records of patients who suffered acute abdominal pain. For each patient the records showed a large number of symptoms and the final diagnosis, to one of nine diseases or diagnostic groups. Most current methods of computer diagnosis use the “Simple Bayes” model in which the symptoms are assumed to be independent, but the present paper does not make this assumption. Those symptoms (or lack of symptoms) which are most relevant to the diagnosis of each disease are identified by a sequence of chi-squared tests. The computer diagnoses obtained as a result of the implementation of this approach are compared with those given by the “Simple Bayes” method, by the method of classification trees (CART), and also with the preliminary and final diagnoses made by physicians.

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Antibiotic Therapy Did Not Prevent the Rupture of Mycotic Aneurysm of the Superior Mesenteric Artery

The Heart Surgery Forum ◽

10.1532/hsf.1322 ◽

2015 ◽

Vol 18 (3) ◽

pp. 088

Author(s):

Ye-tao Li ◽

Xiao-bin Liu ◽

Tao Wang

Keyword(s):

Abdominal Pain ◽

Infective Endocarditis ◽

Aortic Valve ◽

Superior Mesenteric Artery ◽

Mesenteric Artery ◽

Mycotic Aneurysm ◽

Acute Abdominal Pain ◽

Rare Complication ◽

Emergency Operation ◽

History Of

<p class="p1"><span class="s1">Mycotic aneurysm of the superior mesenteric artery (SMA) is a rare complication of infective endocarditis. We report a case with infective endocarditis involving the aortic valve complicated by multiple septic embolisms. The patient was treated with antibiotics for 6 weeks. During preparation for surgical treatment, the patient developed acute abdominal pain and was diagnosed with a ruptured SMA aneurysm, which was successfully treated with an emergency operation of aneurysm ligation. The aortic valve was replaced 17 days later and the patient recovered uneventfully. In conclusion, we present a rare case with infective endocarditis (IE) complicated by SMA aneurysm. Antibiotic treatment did not prevent the rupture of SMA aneurysm. Abdominal pain in a patient with a recent history of IE should be excluded with ruptured aneurysm.</span></p>

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ESTIMATING GAS AND ROCK OUTBURST RISK ON THE BASIS OF KNOWLEDGE AND EXPERIENCE – THE EXPERT SYSTEM BASED ON FUZZY LOGIC

Archives of Mining Sciences ◽

10.2478/amsc-2014-0003 ◽

2014 ◽

Vol 59 (1) ◽

pp. 41-52 ◽

Cited By ~ 5

Author(s):

Norbert Skoczylas

Keyword(s):

Fuzzy Logic ◽

Expert System ◽

Mining Industry ◽

Automatic Assessment ◽

Science Field ◽

Second Stage ◽

Two Stages ◽

Outburst Hazard

Abstract The Author endeavored to consult some of the Polish experts who deal with assessing and preventing outburst hazards as to their knowledge and experience. On the basis of this knowledge, an expert system, based on fuzzy logic, was created. The system allows automatic assessment of outburst hazard. The work was completed in two stages. The first stage involved researching relevant sources and rules concerning outburst hazard, and, subsequently, determining a number of parameters measured or observed in the mining industry that are potentially connected with the outburst phenomenon and can be useful when estimating outburst hazard. Then, the Author contacted selected experts who are actively involved in preventing outburst hazard, both in the industry and science field. The experts were anonymously surveyed, which made it possible to select the parameters which are the most essential in assessing outburst hazard. The second stage involved gaining knowledge from the experts by means of a questionnaire-interview. Subjective opinions on estimating outburst hazard on the basis of the parameters selected during the first stage were then systematized using the structures typical of the expert system based on fuzzy logic.

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