In this review, we present a framework that will enable us to obtain increased accuracy of computer diagnosis in medical patient checkups. To some extent, a new proposition for medical data analysis has been built based on medical data preprocessing. The result of such preprocessing is transformation of medical data from descriptive, semantic form into parameterized math form. A proper model for digging of hidden medical data properties is presented as well. Exploration of hidden data properties achieved by means of preprocessing creates new possibilities for medical data interpretation. Diagnosis selectivity has been increased by means of parameterized illnesses patterns in medical databases.

Two chalcedonite artefacts from the Magdalenian site of Cmiel ow 95 (Poland), with macroscopic features suggestive of thermal treatment, were subjected to a multi-instrumental analysis. The red upper layer of the objects consists of "protohematite", implying temperature-driven, goethite-to-hematite transition. The red layer shows traces of carbonized matter with saccharides and levoglucosan (from burning wood) as well as fatty acids. PXRD data suggest a source of higher temperatures (up to $800 C) within the bottom layer, with $200-300 C range ascribed to the red layer. On the basis of the collected data the artefacts are proposed to be relics of cooking stones.

Abstract. Evaluation of classifiers in diagnosis support systems is a non-trivial task. It can be done in a form of controlled and blinded clinical trial, which is often difficult and costly. We propose a new method for generating artificial medical cases from a knowledge base, utilizing the concept of so-called medical diamonds. Cases generated using this method have features analogous to that of double-blinded trial and, thus, can be used for measuring sensitivity and specificity of diagnostic classifiers. This is easy and low-cost method of evaluation and comparison of classifiers in diagnosis support systems. We demonstrate that this method is able to produce valuable results when used for evaluation of similarity-based classifiers as well as shallow and deep neural networks.