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Advances in medical decision support systems

It is a pleasure to be associated with the 10 papers that appear in this special issue of Expert Systems, The Journal of Knowledge Engineering. The papers in this special issue focus on medical decision support systems and on feature extrac tion=selection for automated diagnostic systems. The foundation for any decision support system is a knowledge base containing the necessary rules and facts, acquired from information and data in the field of interest, in our case medicine. There are three general approaches used to acquire such a knowledge base: traditional expert systems; evidence-based methods; and statistical and artificial intelligence methods. Clinical decision-making is a challenging, multifaceted process. Its goals are precision in diagnosis and the institution of efficacious treatment. Achieving these objectives involves access to pertinent data and application of previous knowledge to the analysis of new data in order to recognize patterns and relations. As the volume and complexity of data have increased, use of digital computers to support data analysis has become a necessity. In addition to computerization of standard statistical analysis, several other techniques for computer-aided data classification and reduction, generally referred to as artificial neural networks, have evolved. Our analysis of recent developments shows that the trend is to develop new methods for computer decision-making in medicine and to evaluate critically these methods in clinical practice. Artificial neural networks have been used in different medical diagnoses and the results were compared with physicians’ diagnoses and existing classification methods. Many of them found that artificial neural networks have more flexibility in modelling and give reasonable accuracy in prediction. What makes artificial neural networks a promising tool is their capacity to find near-optimum solutions from limited or incomplete data sets and the fact that learning is accomplished through training. In addition to these characteristics, it has been shown that artificial neural networks can combine data of different natures, such as those derived from clinical protocols, from laboratory data obtained from measurements and from features from signals and images, into a single system thus forming an integrated diagnostic system. Medical diagnostic decision support systems have become an established component of medical technology. The main concept of the medical technology is an inductive engine that learns the decision characteristics of the diseases and can then be used to diagnose future patients with uncertain disease states. A number of quantitative models including linear discriminant analysis, logistic regression, k nearest neighbour, kernel density, recursive partitioning and neural networks are being used in medical diagnostic support systems to assist human decision-makers in disease diagnosis. Artificial neural networks have been used in many medical diagnostic decision support system applications because of the belief that they have good predictive power. Unfortunately, there is no theory available to guide an intelligent choice of model based on the complexity of the diagnostic task. In most situations, developers simply pick a single model that yields satisfactory results, or benchmark a small subset of models with cross-validation estimates on the test sets (West &West, 2000a, 2000b; Kordylewski et al., 2001; Kwak & Choi, 2002). Various methodologies of automated diagnosis have been adopted; however, the entire process can generally be subdivided into a numDOI: 10.1111/j.1468-0394.2008.00503.x Guest editorial_