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Object oriented artificial neural networks in decision support systems for dermatological research

Shushma Patel

Dan Hanley

Dermatological disorders, although not terminal, cannot be easily treated and are generally a greater problem socially. Usually the disease is contained rather than cured and therefore relapse is a major problem. Treatment of microbial skin diseases is multi-factorial. The onset of diseases such as acne is dependent on many factors, including age, sex, sebum production and hormonal changes. Anti androgens can be as effective a treatment as anti-microbial agents: one targets the sebum, the other the organism. It is a well established fact that with a reduction in the sebum excretion rate, there is a reduction in the number of Propionibacterium acnes, (P. acnes) which correlates with an improvement in acne. It has been shown that an increase in the amount, and changes in the composition of skin surface lipids appear to be directly related to the increasing population of P. acnes around puberty.

Initial research involved both collection over time and analysis of data using traditional statistical methods. Due to the large volume of sparse data it was time consuming and difficult to integrate and implement the methods to support the decision making process. This paper illustrates the value of Object-

Oriented technology and Artificial Neural Networks in building clinical decision support systems to analyse skin surface lipid data from patients with lipid dependent microbial skin diseases. The paper proposes an extension to Blum's framework for analysing data and postulates an architecture for clinical decision support systems.

Health Informatics Journal, Vol. 1, No. 2, 56-68 (1995)
DOI: 10.1177/146045829500100206


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