Desknet: The dermatology expert system with knowledge-based network

D E S K N E T : THE DERMATOLOGY EXPERT SYSTEM WITH KNOWLEDGE-BASED NETWORK. Y o u n ~ O h e Yoon a n d L y n n L. P e t e r s o n , P h . D . D e p a r t m e n t of C o m p u t e r S c i e n c e E n g i n e e r i n g , T h e U n i v e r s i t y of T e x a s a t A r l i n g t o n P.O. B o x 19015, A r l i n g t o n , T X 76019. P a u l R. I ~ e r g s t r e s s e r , M.D. D e p a r t m e n t o f D e r m a t o l o g y , T h e U n i v e r s i t y of T e x a s S o u t w e s t e r n Medical Center, Dallas, TX. Generation of a knowledge base has proved difficult in the development of rule-based expert systems (ES), due to the requirement for explicit rules. This problem arises because of the implicit nature of much of human knowledge (1), especially of expert knowledge. It is known that a domain in which knowledge is implicit can not render a clear and complete specification of decision rules. The knowledge acqusition phase m a y result in the loss of critical information in casting implicit knowledge into explicit rules. Medicine is a domain whose concepts are implicit and difficult for a non-professional such as a knowledge engineer to understand without intensive training. In dermatology, the domain consists of concepts as abstract as those in any area of medicine. Therefore, the Back Propagation algorithm (2) is employed to develop a knowledge base in dermatology, due to the ease and appropriateness of the method in dealing with implicit knowledge. A prototypical connectionist expert system presented in this paper, DESKNET, is built for ultimate use in the instruction of medical students to diagnose papulosquamous skin diseases. Medical students will use DESKNET to test their own diagnoses and to reinforce the sequence of questions employed by dermatologists. DESKNET is an ES composed of a three layered Back Propagation network consisting of an input layer, a hidden layer, and an output layer. The units on a layer have excitatory or inhibitory connections to units on the adjacent layers. Input for the system is a list of s y m p t o m s organized by 18 parameters: duration, itching level, etc. Output is a diagnosis of various papulosquamous skin diseases. Training data consists of 200 sets of symptoms, as input, and an expert's diagnosis, as a desired output, in the form of O's and l's. In DESKNET, knowledge is distributed over the network in such a way that a pattern of weighted interconnections constitutes an implicit specification of decision criteria. The ES uses this network as a knowledge base for diagnosing a new patient given a list of user-supplied s y m p t o m s . A disease is "diagnosed" when represented by a significantly high activation value at an output node. During the interface session, all input data is needed to reach a conclusion because the activation values can change dramatically, based on one input value. Therefore, reasoning with partial data is risky in this problem. However, the system accepts u n k n o w n as the value of a parameter and then eliminates the effects of the parameter on the conclusions. After an initial conclusion is reached, a user can test the effect of a parameter by arbitrarily attributing to it a value of true or f a l s e , and observing the result. The change of activation values as additional parameters are known is presented in a graph which enables a user to analyze the quantifiable effect of each parameter on the activation values. The performance of a trained network is tested with 27 patient cases. The number of correct diagnoses varies with the number of hidden units employed in DESKNET. The increase in the number of hidden units results in higher performance up to a certain point, but additional hidden units do not improve the system's performance. The test cases include several patients who suffer from multiple diseases. For example, the system appropriately diagnoses the skin disease of an AIDS patient as seborrheic dermatitis even though he deviated widely from the classical presentation of seborrheic dermatitis. As in other domains in medicine, large variations may occur in individual cases. The achieved success rate of 75 % is very high and the system performs impressively in this domain. The system is implemented on a VAX 11/780 using the C programming language. The Back Propagation algorithm encodes the implicit "knowledge" such that it remains in an implicit form; thus, more knowledge can be captured. Therefore, the Back Propagation model appears to be a useful tool for representing concepts of a domain and encoding them in a network. However, the Back Propagation algorithm has limitations (3). First, the model fails to represent knowledge in a dynamic domain due to the partial adaptivity of its learning algorithm. New information, such as new parameters of symptoms and diseases, obsoletes the network so that the network has to be retrained. Second, knowledge in a complex domain can be efficiently represented with small subsets used in a hierarchical fashion rather than with a large single set. However, it is difficult to devise the intermediate representation of knowledge in such a complex domain using the Back Propagation algorithm. Resolution of these problems is essential if we are to produce a connectionist expert system capable of coping with a real environment. 1. Polanyi, M. 1958. Personal Knowledge. Chicago: University of Chicago Press. 2. Rumelhart, D. E., Hinton, G. E., and Williams, R. J. 1986. "Learning Internal Representations by Error Propagation, in, D.E. Rumelhart and J. L. McClelland, (Eds.) Parallel Distributed Processing: Exploration in the Microstructure of Cognition, pp. 318:362, Cambridge: MIT Press. 3. Leven, S. and Yoon, Y. 1988. Dynamic Schemas, Expert Systems, and A.R.T. Submitted to the Boston Conference.

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