Intelligent Methodologies for Medical Knowledge Management

In the last years, various methodologies and machine learning techniques have been proposed by the researchers in order to develop efficient knowledge-based systems (KBS) for different tasks. These systems are based on the artificial intelligence methodologies . This paper presents some of the intelligent methodologies for managing and engineering knowledge in KBS. Some of the research results in medical domain, are discussed as well. As time notion is also a very important category in medicine (e.g. time dimension of medical treatment) the paper presents also some temporal solutions that can be used for medical knowledge management. Knowledge representation and intelligent techniques are placed at the heart of all medical knowledge management applications. Knowledge engineers and developers should be aware of the problems while developing these applications and working on them. So, in this paper we evaluate the advantages and disadvantages of some of the intelligent methodologies for managing and engineering knowledge in medical knowledge –based systems. In addition, the paper discusses the major problems of implementing robust techniques in existing e-Health applications and services. Key-Words: Knowledge Management, Knowledge Representation, Machine Learning, Medical Informatics, Knowledge Engineering, Temporal Reasoning. 1.Knowledge representation In order to act intelligently, a computer must have knowledge about the domain of interest. The process of collecting and organizing the knowledge is called knowledge engineering. Although a variety of knowledge representation techniques have been developed over the years [1], these techniques share two common characteristics. First, they can be programmed and stored in memory. Second, they are designed so that the facts and other knowledge contained within them can be manipulated by an inference system, the other major part of the KBS. The inference system uses search and pattern matching techniques on the knowledge base to answer questions, draw conclusions, and perform an intelligent function. 1.1. Logic Logic is the oldest form of knowledge representation technique. In any logical process first, information is given, statements are made, or observations are noted. These form the inputs to the logical process and are called premises. The premises are used by the logical process to create the output which consists of conclusions called inferences. For a computer to perform reasoning using logic, some method must be used to convert the deductive or inductive reasoning process into a form suitable for manipulation by a computer. The result is what is known as symbolic logic or mathematical logic. It is a system of rules and procedures that permit the drawing of inferences from various premises using a variety of logical techniques. These methods are generally known as computational logic. There are two basic forms of computational logic, propositional logic and predicate logic. Since propositional logic deals primarily with complete statements and whether they are true or false, its ability to represent real world knowledge is limited. Consequently, KBS technology uses predicate logic instead. Predicate logic gives added ability to represent knowledge in finer detail. On the other hand, temporal logic is the logic that directly depicts time notion. The shortest motivation for using temporal logic in artificial intelligence systems can be found in [16]: “to introduce temporal relationships (.) it is necessary to broaden the formal apparatus wit temporal logic” (p. 429). The similar way of thinking is to be found in [15], where the authors claim, that to reason about a domain that changes in time it is needed: a) Logic to reason about a state of domain in a certain moment of time, and Recent Researches in Communications and Computers ISBN: 978-1-61804-109-8 193 b) Logic to reason how states of the domain in different moments of time are linked together (pp. 119-120). The temporal logic is a formalism that links the above two aspects of reasoning. 1.2. Lists and Trees Lists and trees are simple structures used for representing hierarchical knowledge. A list is a series of related items. Objects are divided into groups or classes of similar items. Their relationships are shown by linking them together. The simplest form is one list, but a hierarchy is created when two or more related lists are combined. On the other hand, a tree is a graphical structure of hierarchy, it is simply a way of illustrating lists and other hierarchical knowledge. 1.3. Semantic Networks(SN) SNs are graphical depictions of knowledge that show hierarchical relationships between objects. SN which first appeared in 1961 (Masterman’s Net) have many variations. One of them is Sowa’s conceptual graphs. A conceptual graph can be thought of as a graphical representation of a SN. They have a limited possibility of representing time – that is, one can add nodes marked as e.g. “past” or “future”. This is of course much more limited time expression than in temporal logic, for example, it is not possible to express qualitative relations as “earlier”, “later”, “simultaneously” etc. An example of a conceptual graph with time notion is shown in Fig. 1. Fig. 1. A sample conceptual graph with time notion. 1.4. Frames A frame is a relatively large block of knowledge about a particular object, event, location, situation, or other element. The frame describes that object in great detail. The detail is given in the form of slots which describe the various attributes and characteristics of the object or situation With frames, it is easy to make inferences about new objects, events, or situations because they provide a base of knowledge drawn from previous experience. 1.5. Scripts A script is a knowledge representation scheme similar to a frame, but instead of describing an object, the script describes a sequence of events. Like the frame, the script portrays a stereotyped situation. Unlike the frame, it is usually presented in a particular context. To describe a sequence of events, the script uses a series of slots containing information about the people, objects, and actions that are involved in the events. 1.6. Production Rules Production rules, sometimes just referred to as rules or productions, are two part statements that embody small pieces of knowledge. The first part of the rule, called the antecedent, expresses a situation or premise while the second part, called the consequent, states a particular action or conclusion that applies if the situation or premise is true. 1.7. Cases Recent Researches in Communications and Computers ISBN: 978-1-61804-109-8 194 The case is a list of features that lead to a particular outcome. (e.g. the information on a patient history and the associated diagnosis). Determining the appropriate case features is the main knowledge engineering task in case-based systems. This task involves defining the terminology of the domain and gathering representative cases of problem solving by the experts. 2. Expert systems methodology Expert system (ES) is a consultation intelligent system that contains the knowledge and experience of one or more experts in a specific domain [1]. The most commonly systems are rule-based expert systems (RES) and case-based expert systems (CES). In RES, the knowledge base stores the knowledge in the form of production rules (if-then statements). The inference engine contains a set of formal logic relationships which may or may not resemble the way that real human expert reach conclusions. CES uses case-based reasoning (CBR) methodology in which the system can reason from analogy from the past cases. This system contains what is called “case-memory” which contains the knowledge in the form of old cases (experiences). CES solves new problems by adapting solutions that were used for previous and similar problems [2]. The methodology of CBR directly addresses the problems found in rule-based technology, namely: knowledge acquisition, performance, adaptive solution, maintenance. A temporal intelligent system (also a temporal expert system) is an intelligent system, that directly performs temporal reasoning. Therefore, such a system contains not only fact base, rule base and an inference mechanism, but also explicitly concerns time notion. It allows for reasoning about changes of domain in time, for a historical analysis of a domain, for an analysis of future changes and – generally speaking for a dynamic analysis of reality depicted. For these purposes, in the first layer of the system – in the fact base – the temporal logic is used for reality description. The choice of an appropriate temporal logic is not a trivial task, but will not be discussed here. Let us only say that knowledge engineer’s responsibility here is huge. Also in the rule base time notion is present, as the rules are temporal ones, that is in premises and in consequents contain time elements. Certainly, rules are also expressed in temporal logic. Inference mechanism has also to be written in a manner allowing to perceive time and to make use of time notion expressed in fact base and in rule base, that is, to perform temporal reasoning. In [3], two versions of expert systems were developed for heart diseases diagnosis. The first one uses the rule-based reasoning while the second one uses case-based reasoning. The first version is composed of three components: knowledge base, user interface and computational model. The experimental results have shown 76.9% accuracy in estimating the right conclusion. The Second version uses CBR methodology [3]. The systems are able to give an appropriate diagnosis for the presented symptoms, signs and investigations done to a cardiac patient with the corresponding certainty factor. It aims to serve as doctor diagnostic assistant and support the education for the undergraduate and postgraduate young physicians. 3. Intelligent Data mining approach Intelligent data