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Aim, objectives and method
Aim of the research
The research aims at identifying the design issues related to the knowledge bases of medical decision support systems.
Objectives
The following objectives have been achieved to identify the design issues in the knowledge base of medical decision support systems
• Study of knowledge representation schemes used in knowledge based medical decision support systems to identify issues related to knowledge representation
• Study of tools/technologies used in the implementation of knowledge bases and their limitations
• Study of issues related to learning of knowledge base
• Study of issues related to reasoning components of the knowledge based medical decision support systems.
Study of knowledge representation schemes used in knowledge based medical decision support systems to identify issues related to knowledge representation
According to Purcell (2005), the effectiveness of a clinical decision support system is dependent on the design of its knowledge base. The success of a clinical support system from the accuracy point of view requires the proper analysis and design of its knowledge base. One of the most important design issues that may lead a medical decision support system towards inaccurate diagnosis is the representation of knowledge in the knowledge base. According to Carter (1999), knowledge representation deals with providing information to the intelligent systems belonging to a particular domain for efficient processing. A lot of knowledge representation schemes are available and have been used to represent knowledge in medical decision support systems for example logic, procedural, graph/network, and structured etc. Each of the representation schemes have some advantages as well as disadvantages associated with it (Kong et. al, 2008). These representation schemes are briefly described as follows
Medical decision support systems using logic as a knowledge representation scheme in the knowledge base usually have TRUE-FALSE structure and inference mechanism is just a lookup of the relevant facts (Kong et. al, 2008). However, the disadvantage of the logic representation is that the process of problem solving may become obscure when there is an increase in the number of related facts in the knowledge base. Ultimately it results in an increase in the number of ways these facts can be combined to infer a correct result (Bingi, Khazanchi, &Yadav, 1995).
Procedural knowledge representation scheme represents the knowledge in the form of rules. Medical decision support systems such as MYCIN, PUFF, UMLS and Chinese medical diagnostic systems (CMDS) are rule based. But the problem with rule based representation systems is that they do not create a correlation between the clinical signs and symptoms (Kong et. al, 2008) which cause in an immature inference by the inference mechanism and ultimately causes in an incorrect diagnosis.
Also networks such as Bayesian belief networks, decision trees and neural networks have been used in clinical decision support systems for representing knowledge (Kong et. al, 2008). All of these representation schemes are good in representing conditional dependencies and probabilistic inference (Montani & Terenziani, 2006). But the problems are also associated with these representation schemes e.g. artificial neural networks have capability to learn on the basis of data they have observed, but their disadvantage is that they can not give consistent representation of the knowledge that is not relevant to their learnt knowledge (Kong et. al, 2008).
The structural representation uses the frame format to represent the widely accepted knowledge and was introduced by Minsky. CENTAUR and Arden Syntax use frame format to represent the knowledge (Kong et al, 2009).
After a study of the literature on knowledge based medical decision support systems in general and on the knowledge representation scheme and systems developed on the basis of these schemes, issues have been identified that cause in an inaccurate diagnosis.
Study of tools/technologies used in the implementation of knowledge bases and their limitations
Knowledge that has been represented in the form of rules or any other formalism in a knowledge base is actually encoded in the memory of computer by means of programming languages and tools. The knowledge represented or encoded in the medical knowledge base is useless if it can not be accessed or retrieved accurately (Shortliffe, 1986). The deficiency of a programming tool may restrict the retrieval of logical and related information. Further significant issues have been identified relevant to tools and technologies in chapter 3.
Identification of issues in the learning of knowledge base
Another deficiency that pertains to the accurate retrieval of diagnostic information lies in the features of a particular learning scheme used. The learning techniques and algorithms are meant to create relationships between the symptoms and diseases. Artificial neural networks learn on the basis of data they have observed, but behave in an inconsistent way for the knowledge that is different from the learnt knowledge (Kong et. al, 2008). Issues related to learning techniques such as artificial neural networks and genetic algorithms have been discussed and presented in chapter
Study of issues related to reasoning components of the knowledge based medical decision support systems
Although the reasoning component of a medical decision support system is not a part of its knowledge base, yet they both are tightly coupled with each other. Reasoning in knowledge based systems is typically based on the representation of knowledge. More formal representation of knowledge provides better reasoning support about the decision support system. A lot of studies are available covering the complexities in medical tasks. The reason for these complexities may be differences in expertise in reasoning with the system and also a variation of the reasoning strategies used by the physicians (Patel & Groen, 1986). A study of d reasoning strategies such as case based reasoning and rule based reasoning in medical diagnostic decision support systems has been carried out to investigate the issues leading to imprecise diagnosis
Study of knowledge representation schemes, tools and technologies, learning schemes/techniques, reasoning components and issues related to them has been presented in next chapter.
Research Method
To carry out the proposed research on identification of design issues in the medical decision support systems, literature survey has been conducted. More than 120 research articles on knowledge based medical decision support systems were collected and around 71 articles were selected for the study. Articles were selected on the basis of their relevance to the desired aim and objectives.
Search engines and different research databases were used to find the relevant research articles on knowledge based medical decision support systems in general and the design issues of their knowledge bases in special. Following resources were accessed and used to accomplish the task:
• Google search engine
• Google Scholar
• ELIN database
• ACM Portal
• IEEE Explore
• Citeseer
• Science Direct
• Scopus
• Academic Search Elite
List of keywords
The following keywords were used to find the relevant literature using different search engines and databases:
• MDDS Design issues
• Design issues of knowledge bases
• Design limitations
• Constructing knowledge bases
• Knowledge base limitations
• Knowledge representation in MDDS
• Limitations of knowledge representation
• Logic representation problems
• Learning issues in knowledge bases
• Learning in artificial neural networks
• Limitations of supervised learning
• Limitations of unsupervised learning
• Limitations of knowledge repositories
• Problems with declarative languages
• Limitations of case based reasoning
• Limitations of rule based reasoning
• Shortcomings of reasoning strategies
• Implementation of knowledge bases
• Knowledge base implementation tools
• Design by contract
• Semantic web in medical diagnosis
• Protégé
Literature Survey
A variety of factors cast their profound effects in retrieving accurate advice from the knowledge based medical system. Those factors as described in section 1.2 include knowledge representation, tools and technologies used for implementing knowledge bases, learning techniques and the reasoning mechanism of the knowledge based system
To identify the design issues specific to knowledge bases of the medical decision support systems, literature on medical decision support systems was studied. This section presents the issues and limitations of the factors contributing to the design of knowledge bases of decision support systems in general and medical decision support systems in particular.
Study of issues related to knowledge representation schemes
Knowledge representation deals with providing information to the intelligent systems belonging to a particular domain for efficient processing (Carter, 1999). Clinical decision support systems use various formalisms for representation of medical knowledge, but still research on this important component of a knowledge base design has not been a driving force for clinical decision support in the last few years (Peleg & Tu, 2006).
Carter (1999) has classified the knowledge representation schemes into logic, procedural, graph/network and structured knowledge representation schemes. Also there exist other knowledge representation schemes such as temporal and spatial knowledge representation schemes (Allan, 1984; Shahar & Musen, 1993), but this research has not focused on the issues associated with these representation schemes. As stated in previous section, each of the knowledge representation mechanisms mentioned above has advantages and disadvantages associated with it.
Study of Logic representation scheme
Logic is one of the ancient mathematical and philosophical representations of the knowledge. With concrete syntax and vocabulary logic is considered as one of the most mature problem solving mechanisms (Bingi et. al, 1995). Knowledge representation schemes based on logic usually consist of declarative statements with Boolean operators such as AND, OR and NOT and TRUE, FALSE structure (Kong et. al, 2008). Medical diagnostic systems have used various formalisms for knowledge representation including causal, anatomic, taxonomic, heuristic, functional and safety models (Lucas, 1995). These systems include CASNET/GLAUCOMA (Kulikowski & Weiss, 1982), ABEL (Patil et al., 1982), Oxford System of Medicine (OSM) (Fox et al., 1990) and DILEMMA system (Huang et al., 1993) etc.
Causal model works on principle Cause (x, y), which means that y is effect of cause x (Lucas, 1995). Example of causal model of logic in medical diagnosis is presented below. The symptoms for disease malaria are mapped to generate relationship between them as presented below and presence of all the symptoms in patient simply indicates the malaria.
A = fever, B = headache, C = Vomiting, D = Dizziness A AND B AND C AND D -> Malaria
Although logic handles the incomplete knowledge in an impressive way yet there are limitations that are associated with the medical systems using logic as a knowledge representation mechanism. Concepts are represented in very general terms e.g. In DILEMMA system, despite of the fact that reasoning is performed in applying the concepts in stages by suggesting the candidate solutions and then further refining them (Lucas, 1995), yet such type of approach is not practically being used in medical and clinical diagnosis. The process of reasoning and diagnosis with such systems becomes delicate and requires more domain as well as technical knowledge by the physicians. Another disadvantage of the logic is that it is difficult to determine how to use the facts stored in the data structure of the system (Bingi et. al, 1995). The same was the reason for the incorrect diagnosis by INTERNIST-I, because it lacked in representing the relationship between the manifestations or symptoms and the diseases (Wolfram, 1995). Another limitation of logic scheme becomes apparent with the increase in number of facts in knowledge base, which ultimately increases the number of ways to join them, hence increasing complexity (Bingi et. al, 1995). Applying the same analogy on symptoms and diseases may result in imprecise diagnosis because many diseases may have lot of similar symptoms and to create meaningful relationship is difficult.
Study of Procedural representation
Procedural knowledge representation makes use of IF-THEN rules and is helpful in diagnosis and therapeutic decision making (Carter, 1999). Expert systems using rules as knowledge representation scheme in medical decision support systems domain include MYCIN, PUFF, UMLS and CMDS etc (Kong et. al, 2008). Rules have been the principal formalism for knowledge representation in medical expert systems to provide decision assistance to physicians in diagnosing diseases. Applying the symptoms of malaria in form of rules gives the following interpretation:
Also an excerpt from the MYCIN rules (Melle, 1978) is presented below:
Figure 2: An excerpt from the MYCIN rule, source (Melle, 1978)
However, some problems are coupled with procedural representation of knowledge in medical expert systems because representation of relationships of medical facts such as symptoms, diseases and medicine etc may be somewhat difficult.
A major inefficiency of a procedural knowledge representation scheme becomes apparent when there is a large search space (Barr & Feigenbaum, 1981). Since the knowledge is represented in modular form, thus the search efficiency depends on how correctly a particular rule has been applied and inference has been made. As the knowledge domain grows large, the probability of selecting and applying the appropriate rule decreases. Consequently the accuracy of inference engine counteracts the accuracy achieved by using problem solving heuristics (Baldwin & Kasper, 1986). The applicability of this limitation is vital for knowledge based medical decision support systems as they are all about creating and identifying relationships among the symptoms, diseases, medicine etc. The second problem with the procedural knowledge representation based on rules is that rules are very poor in expressing the incomplete knowledge (Reichgelt, 1991; Davis, Buchana & Shortliffe, 1977). The reason claimed for this inability is that it is difficult to follow the control flow in programming languages than the algorithmic representations (Bingi et. al, 1995). This might be due to the inherent limitations of a representational language, but obviously to interpret the partial and deficient information might not be possible even for a perfect programming tool. Another problem with rule based knowledge representation systems in medical domain is that they do not create a correlation between the clinical signs and symptoms due to existence of pre-symptoms and post symptoms (Kong et. al, 2008), finally resulting in immature inference and diagnosis. The fourth shortcoming of procedural knowledge representation identified by Davis et. al, (1977) while evaluating the MYCIN expert system is regarding the control structure that is based on backward chaining. Davis et. al. (1977, p. 33) justify their claim by stating that:
“ It is not always easy to map a sequence of desired actions or tests into a set of production rules whose goal-directed invocation will provide that sequence. Thus, while the system’s performance is reassuringly similar to some human reasoning behavior, the creation of appropriate rules which result in such behavior is at times non-trivial”
Study of Network representation scheme
As stated earlier, different types of networks such as Bayesian belief networks, decision trees and neural networks have been used in clinical decision support systems for representing knowledge (Kong et. al, 2008). Despite of their capabilities of handling conditional dependencies and probabilistic inference (Montani & Terenziani, 2006), still there representational mechanism lacks in certain aspects. A brief description of working of Bayesian Networks and artificial neural networks has been provided below to highlight issues related to them.
Bayesian Networks also called as Bayesian belief networks or causal probabilistic networks are based on mathematical representation of knowledge and are useful in representing conditional dependencies (Kong et. al, 2008; Miller, 1994). Various medical diagnostic systems based on Bayesian networks have been developed for diagnosis of various diseases like cancer and also having applications in radiology and ICU. In these types of networks relationship between symptoms and diseases is represented through nodes and paths between them. For example, the symptoms for malaria and hepatitis are as follows:
Malaria A = fever, B = Headache, C = Bodyache, D = Vomiting Hepatitis A=Fever, B=Headache, C=Malaise, D=Vomiting, E=Jaundice
It can be seen that some symptoms like fever, headache and vomiting are common. In order to represent the symptoms for both the diseases the knowledge may be codified as follows
A=Fever, B=Headache, C= Bodyache, D=Vomiting, E=Malaise, F= Jaundice The symptoms and diseases are represented in the network form as in following figure
The given network can calculate the probabilities of existence of various diseases based on probabilistic relationship. However, a major problem with such networks is that as these networks consist of nodes and paths, so to make an inference for a node having more than one path is NP hard or computationally intractable (Miller, 1994). It ultimately results in confusion in correctly diagnosing the disease. Niedermayer (2008) describes the problem of Bayesian Networks while discussing their applications in general. If that problem is applied in medical domain then it can be stated that, an unforeseen symptom of a disease may not be correctly handled by the medical system, because the system may contravene the probabilities upon which it is developed Another type of network based knowledge representation scheme discussed is artificial neural networks. A variety of artificial neural networks have been used in clinical decision support applications including stochastic networks, recurrent networks and feedforward neural networks etc. Artificial neural networks are constructed by representing the knowledge in a way similar to that of a human brain. Artificial neural networks are composed of neurons for processing the information. These networks have been widely used in medical decision support systems for knowledge representation and making inference (Maglogiannis, 2009)
Table of contents :
1. Introduction
1.1. Background
1.2. Problem
2. Aim, objectives and method
2.1 Aim of the research
2. 2 Objectives
2.3 Research Method
2.4 List of keywords
3. Literature Survey
3.1 Study of issues related to knowledge representation schemes
3.2 Study of issues related to tools and technologies
3. 3 Study of issues related to learning
3.4 Study of issues related to reasoning strategies
4. Analysis
5. Conclusion
5.1. Future work
6. References
