Implementation of XpertMalTyph: An Expert System for Medical Diagnosis of the Complications of Malaria and Typhoid

The dearth of medical experts in the developing world has subjected a large percentage of its populace to preventable ailments and deaths. Also, because of the predominant rural communities, the few medical experts that are available always opt for practice in the few urban cities. This consequently puts the rural communities at a disadvantage with respect to access to quality health care services. In this work, we designed and implemented XpertMalTyph; a novel medical diagnostic expert system for the various kinds of malaria and typhoid complications. A medical diagnostic expert system uses computer(s) to simulate medical doctor skills in diagnosis of ailments and prescription of treatments, hence can be used to provide the same service in the absence of the experts. XpertMalTyph is based on JESS (Java Expert System Shell) programming because of its robust inference engine and rules for implementing expert system

Differential diagnosis of dementia:a comparison between the expert system EVINCE and clinicians

The diagnostic performance of the expert system EVINCE was compared with that of 85 clinicians in diagnosing 10 patients suspected of suffering from dementia. A multidisciplinary expert committee provided a standard diagnosis as reference for comparison. The results showed that the syndrome and etiologic diagnoses made by EVINCE were in very close agreement with those of the expert committee and that the diagnostic performance of EVINCE was better than that of the average clinician. The present findings indicate that expert systems, especially those within the realm of complex multidimensional medical problems, could be a valuable aid in medical practice

Linking medical records to an expert system

This presentation will be done using the IMR-Entry (Intelligent Medical Record Entry) system. IMR-Entry is a software program developed as a front-end to our diagnostic consultant software MEDAS (Medical Emergency Decision Assistance System). MEDAS (the Medical Emergency Diagnostic Assistance System) is a diagnostic consultant system using a multimembership Bayesian design for its inference engine and relational database technology for its knowledge base maintenance. Research on MEDAS began at the University of Southern California and the Institute of Critical Care in the mid 1970's with support from NASA and NSF. The MEDAS project moved to Chicago in 1982; its current progress is due to collaboration between Illinois Institute of Technology, The Chicago Medical School, Lake Forest College and NASA at KSC. Since the purpose of an expert system is to derive a hypothesis, its communication vocabulary is limited to features used by its knowledge base. The development of a comprehensive problem based medical record entry system which could handshake with an expert system while creating an electronic medical record at the same time was studied. IMR-E is a computer based patient record that serves as a front end to the expert system MEDAS. IMR-E is a graphically oriented comprehensive medical record. The programs major components are demonstrated

Expert System for Diagnosing Spine Diseases Using the Forward Chaining Method

An expert system is a technology that is able to integrate medical knowledge and data processing to diagnose spinal cord diseases. With various algorithms and a carefully structured knowledge base, this expert system can identify spinal cord diseases based on the symptoms presented by the patient. The main advantage of this expert system is its ability to process data quickly and provide diagnostic recommendations consistently. The results provided from this research are an expert system for identifying spinal cord disease which was built using the Visual Basic software application system. From patient data and symptom data applied to the expert system for in spinal cord disease, it is known that the accuracy of the system for diagnosing spinal cord disease is 95% of patients

Developing indicators of pattern identification in patients with stroke using traditional Korean medicine

Abstract Background The traditional Korean medical diagnoses employ pattern identification (PI), a diagnostic system that entails the comprehensive analysis of symptoms and signs. The PI needs to be standardized due to its ambiguity. Therefore, this study was performed to establish standard indicators of the PI for stroke through the traditional Korean medical literature, expert consensus and a clinical field test. Methods We sorted out stroke patterns with an expert committee organized by the Korean Institute of Oriental Medicine. The expert committee composed a document for a standardized pattern of identification for stroke based on the traditional Korean medical literature, and we evaluated the clinical significance of the document through a field test. Results We established five stroke patterns from the traditional Korean medical literature and extracted 117 indicators required for diagnosis. The indicators were evaluated by a field test and verified by the expert committee. Conclusions This study sought to develop indicators of PI based on the traditional Korean medical literature. This process contributed to the standardization of traditional Korean medical diagnoses.</p

DEVELOPMENT OF A PROTOTYPE OF MALARIA CLINICAL DIAGNOSTIC DECISION SUPPORT SYSTEM

Introduction : Malaria is a public health problem that still causes mortality, particularly in high risk population. Kabupaten Nias is one of the malaria endemic areas. Malaria diagnosis is mainly determined according to physical examination, despite the fact that laboratory examination is the gold standard of malaria diagnosis. To help health workers in diagnosing malaria accurately, it is necessary to develop a decision support system for malaria diagnosis.Objectives: To develop a prototype of malaria diagnostic decision support system.Methods: It was a descriptive study with action research design to explore each phase in the development of a prototype of malaria diagnostic decision support system. Participants of the study consisted of 5 general practitioners in RSU Gunungsitoli and 2 nurses in Puskesmas Gunungsitoli.Results: The study created an application model of computer-based malaria diagnostic decision support system designed using PHP programming language and MySQL database. This system worked by entering malaria clinical symptoms into the expert system, and data of symptoms were processed by the expert system to determine diagnosis and medical advice that was useful to assist health staff in making decision.Conclusion: Malaria diagnostic decision support system that had been developed attracted the interest of health workers and help them in diagnosing malaria clinically.Keywords: diagnostic decision support system, prototype, malaria, computer application, Gunungsitol

The WIC Advisor: A Case Study in Medical Expert System Development

This project provides a good case study of expert system development with untrained experts over a short period of time. We describe the development of a working medical screening and diagnosis expert system for use at the Women, Infants and Children (WIC) clinics in Madison County, Illinois. The system was designed and implemented over the period of four months. A large number of knowledge acquisition techniques were employed, some of them customized in ways that greatly increased their effectiveness. This paper explores the development of THE WIC Advisor, from problem definition through expert system delivery. The knowledge acquisition methods used in creating The WIC Advisor comprise a beneficial case study of several traditional techniques. Protocol analysis, question/answer listing, knowledge acquisition room selection, prototyping, focused interviewing, multiple expert selection, direct questioning, audio-tape transcription, diving the domain, role-playing and teach back were employed [4,5]. Important factors in the success of this expert system were the selection of a limited diagnostic domain, the choice of multiple experts who worked well together, and our continuing efforts to make the experts feel comfortable with the technology and the process. The major benefits of the system include assisting clients between clinic visits, cataloging basic medical data, and providing consistent and verifiable informatio

An Expert System To Determine The Bone Diseases

An expert system named BONNEX to diagnose bone diseases, being one of artificial intelligence (AI) applications, has been developed in this work. This expert system is utilizing the expertise from orthopedics and other resources. BONNEX is used to perform diagnoses based on patient's data, clinical examination data and other relevant sources in the same way as an expert can do. With the help of the expert system, the non experts can perform the decision making process in the same wayas the experts. BONNEX would make experts' diagnostic capability more widely available in the clinical community. BONNEX will aid inexperienced orthopedics or general practitioners working in small medical centers and rural areas to diagnose the nature and conditions of the bone diseases amongst patients before it can be referred to the expert doctors and orthopedic who will be normally be available in the big hospitals or medical centers which are far away from patients' location. It is hoped that with the early detection of the bone diseases, further and immediate therapies can be taken to cure the diseases The elements needed in developing the expert system are user interface, database, knowledge base, explanation facility and inference engine. The users interact with the system through a user interface. The database of the system contains expert-level knowledge on bone diseases and the information is obtained from interviews with the expert orthopedics and research from books, websites and journals. After the bone diseases are determined, the signs and symptoms of the diseases are verified and classified into three categories. The data is re-arranged into systematic representation. The decision table is prepared for the system. The decision table is the representation of the findings to ease the expert system development. It lists out the diseases in one axis and the corresponding symptoms into another axis