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Research and Application of TCM Sub-health Associate Diagnosis System Based on Domain Ontology

Author: GuLin
Tutor: XiaYouMing
School: Yunnan Normal University
Course: Computer Software and Theory
Keywords: Knowledge Acquisition Knowledge Representation Knowledge is stored Noumenon Domain ontology Fuzzy Reasoning TCM Sub-health Aided diagnosis Expert system Case Study
CLC: TP391.5
Type: Master's thesis
Year: 2008
Downloads: 67
Quote: 0
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Medical Expert System is an important direction in the application of artificial intelligence technology, the field of medical diagnosis is one of the core areas of the medical expert system, because of the lies of the medical diagnostic techniques. Medical diagnostic aid expert system is the promotion of the use of expert systems design principles, with a large number of experts, of valuable theoretical as well as rich clinical experience of the medical expert system in the field of medical diagnostics, simulation of medical experts to diagnose the thinking process of the disease to help doctors solve complex medical problems, can be regarded as the doctor diagnosed auxiliary tools even directly aided diagnosis of common diseases and do not necessarily need the participation of doctors. The expert system of the information processing of medical diagnostic aids is based on knowledge of the system of the intelligent inference, in function, it is in the medical field, the expert system program of the level of ability to solve problems. It comes to knowledge acquisition, knowledge representation, knowledge storage and inference control mechanisms and intelligent man-machine interface research, artificial intelligence and domain knowledge in one system, is a very broad prospects for application areas. In this paper, the electronic medical record form of semi-automatic acquisition of knowledge. Chinese medicine experts in the diagnosis and treatment of patients, and fill out the electronic medical record at the same time as assistants or experts, knowledge acquisition program will automatically representation in accordance with the definition of a good framework, knowledge of the electronic medical record stored in the text, and then the knowledge engineer of these medical records knowledge , respectively, to form the intellectual framework and case frame, and finally the framework of two kinds of knowledge stored in the knowledge base and case library. Specifically addressed the problem of matching patients the readme with standard symptoms based electronic medical record and fuzzy method. In this paper, the study of the body and its built abstract domain ontology formal methods for TCM subhealth domain knowledge, and knowledge of the field of traditional Chinese medicine diagnosis ontology Formal Specification and design. Domain ontology driven acquisition, based on the knowledge of Chinese medical record symptoms of sub TCM auxiliary diagnostic knowledge reasoning based on fuzzy inference method. And introduced in Chapter 4.5 Knowledge of analysis and reasoning based on the axiom of TCM pulse diagnosis. Finally, the above method is applied to the field of traditional Chinese medicine sub-health auxiliary diagnosis, specifically introduced TCM subhealth auxiliary diagnostic knowledge (rules) library, symptoms library syndrome database analysis and design, and realized TCM subhealth auxiliary diagnostic expert system syndrome reasoning process. An important branch of the expert system (Expert System, ES) as an artificial intelligence in the early 1960s and developed a new applied science, and With the continuous development of computer technology is getting more sophisticated and mature. The expert system is an intelligent computer program. This program uses the knowledge and reasoning processes for solving complex problems who need the expertise of the outstanding figures in order to solve difficult and complex practical problems can be resolved like a pro computer (software) systems. Knowledge representation to describe the world made a set of conventions, is a symbol of knowledge and formal modeling, various knowledge representation method, various formal knowledge model. Research is necessary to consider the representation of knowledge stored knowledge representation, but also consider the use of knowledge. Natural language and computer communication, humanity has long pursued. Natural language recognition and processing is one of the most important topics in artificial intelligence research, is also the key to artificial intelligence research. How to get a variety of knowledge, and can use a computer and processing method to express knowledge is the fundamental problem of knowledge acquisition. Owns knowledge is an important symbol of the expert system is different from other computer software systems, and the quality and quantity of knowledge are key factors determine the performance of the expert system, but how to make the expert system to obtain high-quality knowledge, it is to solve the knowledge acquisition problems. The basic task of knowledge acquisition for expert systems to acquire knowledge, to establish a sound, perfect, effective knowledge base to meet the needs of solving field problems. Learning methods of artificial intelligence in a case study (CBI), the method is much the same range of learning strategies, relying on past experience, learning and solving problems. New case can modify the case library is similar to the current situation old case to get. Ability of case-based reasoning technology try knowledge finishing fusion, the extraction of the issues related to the event or knowledge of the case. Ontology is an explicit specification of a conceptualization. The body can represent different things: Glossary and data dictionaries, thesauri and classification framework and data model, formal ontology and reference. A body is actually a controlled vocabulary of some kind of body language, the language syntax rules limited vocabulary expressed in terms of the content of the specific areas, the grammar formally specifies how the body controlled vocabulary term common use. As the two dimensions of the level of detail and the field dependence of the body by a high level of detail, called the reference ontology, called low level of detail shared ontology. In accordance with the field dependence can be subdivided into four categories of top-level, domain, task and application ontology. In addition, depending on the subject can be divided into the knowledge representation body, universal ontology, domain ontology, the term ontology and task ontology; completely according to the level of formality is divided into non-formal, non-formal structure. Semi-formal, formal ontology. Scientific research and the daily life of the people have been in pursuit with a mathematical model to determine the Cantor set concept to solve the problem or to characterize the phenomenon. But the expert system's problem solving generally do not like mathematics, physics and other disciplines, as rigor and accuracy, it processes information is often uncertainty, imprecision, do not fully know, even vague, incomplete . There are two main reasons for this phenomenon: First, the reasoning in accordance with the rules (or knowledge) imprecise, imperfect, and different schools or inconsistent; evidence itself uncertain, incomplete and even interference . Therefore, the use of expert system design imprecise reasoning, is almost difficult to avoid, sometimes becomes an important issue related to the success of expert system design. Representative of the following four methods: deterministic theory, subjective Bayes methods, evidence theory, fuzzy set theory. Regardless of what kind of imprecise reasoning model, they deal with the problem of the basic ideas and methods are very different, but the essence is the same, that is, have the same structure, and that the following three parts: 1) knowledge uncertainty description; 2) evidence of uncertainty description; 3) uncertainty update algorithm. The core idea of ??inexact reasoning rule-based expert system for each of the axioms given an uncertainty measure, and then presents a set of algorithms, on this basis, it can be the set of algorithms, not by the axiom uncertainty uncertainty theorem is obtained. Fuzzy set theory is a an excellent way to handle fuzzy phenomenon. It is more used in predictive expert system, such as economic forecasts, weather forecasting, strategic deployment. The reason of it causes uncertainty is caused by the fuzziness. It uses the membership function of this efficiency calculation method to express uncertainty. The core idea is to determine such as: the possibility of the possibility of distribution, the probability distribution function, the conditional probability distribution function, the relationship between the marginal probability distribution function, and several other metrics and they ask, and the conversion of various fuzzy proposition rules and imprecise propositions inference rules.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Diagnostic Machine
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