PubMed · 8484881
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W Goossen. 1993-04-01. [Information needs by nurses].. https://pubmed.ncbi.nlm.nih.gov/8484881/
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One would like to have a ranking method for diagnostic tests that justifiably makes no assumptions about the local 'disease mix', that is, about the pre-test probabilities of the diagnostic alternatives. Assuming the simplest possible clinical context we therefore examine the following question: can a summary index of diagnostic test performance be devised which is loyal to the expected utility paradigm and, at the same time, is incidence-free (prevalence-free)? In other words, regardless of pre-test probabilities, the index should rank rival tests in a way which agrees with how they might be ranked by expected utility calculations. From a practical point of view, the answer is negative; it is proved that such indices do exist, but they necessarily impose a very restrictive mathematical form on the utility framework and, what is worse, invariably go against what is medically appropriate by violating a boundedness requirement.
The application of artificial neural networks (ANNs) for prognostic and diagnostic classification in clinical medicine has become very popular. In particular, feed-forward neural networks have been used extensively, often accompanied by exaggerated statements of their potential. In this paper, the essentials of feed-forward neural networks and their statistical counterparts (that is, logistic regression models) are reviewed. We point out that the uncritical use of ANNs may lead to serious problems, such as the fitting of implausible functions to describe the probability of class membership and the underestimation of misclassification probabilities. In applications of ANNs to survival data, further difficulties arise. Finally, the results of a search in the medical literature from 1991 to 1995 on applications of ANNs in oncology and some important common mistakes are reported. It is concluded that there is no evidence so far that application of ANNs represents real progress in the field of diagnosis and prognosis in oncology.