Medical Expert Systems Survey

There is an increase interest in the area of Artificial Intelligence in general and expert systems in particular. Expert systems are rapidly growing technology. Expert system is a branch of Artificial Intelligence which is having a great impact on many fields of human life. Expert systems use human expert knowledge to solve complex problems in many fields such as Health, science, engineering, business, and weather forecasting. Organizations employing the technology of expert system have seen an increase in the efficiency and the quality. An expert system is computer program that emulates the behavior of a human expert. The expert system represents knowledge solicited from human expert as data or production rules within a computer program. These rules and data can be used to solve complex problems. In this paper, we give an overview of this technology and will discuss a survey on many papers done in health using expert system

Hybrid Fuzzy Medical Expert Systems

Expert Systems are intelligent programs of Artificial Intelligence (AI). In many applications, information available to the expert system is incomplete like medical diagnosis. This incomplete information is fuzzy rather than probable. Hybrid fuzzy expert systems (HFMES) combination of different fuzzy expert systems of same type co-ordinate and co-operated. In this paper, Hybrid fuzzy medical expert Systems are studied. Fuzzy inference and fuzzy reasoning are discussed for HFMES Fuzzy knowledge representation is disused for HFMES. Some examples are given for HFMES

Hybrid Fuzzy Medical Expert Systems

Expert Systems are intelligent programs of Artificial Intelligence (AI). In many applications, information available to the expert system is incomplete like medical diagnosis. This incomplete information is fuzzy rather than probable. Hybrid fuzzy expert systems (HFMES) combination of different fuzzy expert systems of same type co-ordinate and co-operated. In this paper, Hybrid fuzzy medical expert Systems are studied. Fuzzy inference and fuzzy reasoning are discussed for HFMES Fuzzy knowledge representation is disused for HFMES. Some examples are given for HFMES

Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types

Fuzzy neural network methodology applied to medical diagnosis

This paper presents a technique for building expert systems that combines the fuzzy-set approach with artificial neural network structures. This technique can effectively deal with two types of medical knowledge: a nonfuzzy one and a fuzzy one which usually contributes to the process of medical diagnosis. Nonfuzzy numerical data is obtained from medical tests. Fuzzy linguistic rules describing the diagnosis process are provided by a human expert. The proposed method has been successfully applied in veterinary medicine as a support system in the diagnosis of canine liver diseases

Revolutionizing Healthcare: The Role of AI-Based Medical Expert Systems in Building a Better Future

Modern society has an increasing need for better architecture and medical care. However, this difficulty is not sufficiently addressed by present medical architecture. The Medicinal Expert technique can be used to help persons in need in order to address this issue. A tremendous amount of medical data, including patient medical histories, records, and new medications, can be managed and maintained using this technology. It can help with decision-making and fill in for specialists when they are not present. The Medicinal Expert approach is a complex computer software system that generates forecasts using empirical data and expert knowledge. Based on the available training data and knowledge base, these systems function intelligently. Additionally, there are numerous Medical Expert System tools that support clinicians, help with diagnosis, and are crucial for instructing medical students. In this study, we introduce an AI-based Medical Expert System, its features, and its potential to help patients and medical students. We also go through some key findings from recent and prior research on expert systems, as well as how these systems can make the world a better place

A Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to Knowledge Acquisition

Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to knowledge acquisition is proposed in this paper as a viable solution to the challenges of rule-based unwieldiness and sharp boundary problem in building a fuzzy rule-based expert system. The fuzzy models were based on domain experts’ opinion about the data description. The proposed approach is committed to modelling of a compact Fuzzy Rule-Based Expert Systems. It is also aimed at providing a platform for instant update of the knowledge-base in case new knowledge is discovered. The insight to the new approach strategies and underlining assumptions, the structure of FARME-D and its practical application in medical domain was discussed. Also, the modalities for the validation of the FARME-D approach were discussed