Abstract: A method of evaluating gastrointestinal cancer risk. The method comprises generating a set of features comprising a plurality of current blood test results from a blood collected from a target individual, providing at least one classifier generated according to an analysis of a plurality of respective historical blood test results of each of another of a plurality of sampled individuals, and evaluating, using a processor, a gastrointestinal cancer risk of the target individual by classifying the set of features using the at least one classifier.

Abstract: A method of evaluating lung cancer risk by generating a set of features comprising a plurality of current blood test results from a blood collected from a target individual, providing at least one classifier generated according to an analysis of a plurality of respective historical blood test results of each of another of a plurality of sampled individuals, and evaluating, using a processor, a lung cancer risk of said target individual by classifying said set of features using said at least one classifier. Each of said plurality of historical and current blood test results comprises results of one or more selected blood tests such as white blood cells blood test results, biochemistry test results, platelets blood test results including at least one of platelets count and mean platelet volume (MPV) and/or a combination of smoking history and red cell test results.

Abstract: A method of evaluating lung cancer risk by generating a set of features comprising a plurality of current blood test results from a blood collected from a target individual, providing at least one classifier generated according to an analysis of a plurality of respective historical blood test results of each of another of a plurality of sampled individuals, and evaluating, using a processor, a lung cancer risk of said target individual by classifying said set of features using said at least one classifier. Each of said plurality of historical and current blood test results comprises results of one or more selected blood tests such as white blood cells blood test results, biochemistry test results, platelets blood test results including at least one of platelets count and mean platelet volume (MPV) and/or a combination of smoking history and red cell test results.

Abstract: There is provided a method of computing computed pathological indication(s), comprising: receiving an indication of values of current laboratory test results calculated based on an automated analysis of laboratory sample(s) collected from a target individual, selecting classifier(s) according to an analysis of the indication of values of the current laboratory test results, determining additional laboratory test(s) according to the analysis of the indication of values of the current laboratory test results and/or the selected classifier(s), generating instructions for automatic execution of second automatic laboratory testing device(s) on the laboratory sample(s) to obtain a second indication of a second value of the additional laboratory test(s), and evaluating computed pathological indication(s) by applying the selected classifier(s) to the indication of values of the current laboratory test results and the second indication of the second value of the additional laboratory test result(s).

Abstract: There is provided a method, comprising: receiving an indication of medical test results of a target individual, applying a trained classifier to the indication to compute a set of clinical outcome prediction scores, wherein each respective score is indicative of a prediction of a certain pathology of a certain physiological system of the target individual, computing for the set of clinical outcome prediction scores of the target individual, a cluster of sets of computed clinical output predictions for individuals, according to a requirement of a statistical distance, wherein each set of clinical outcome prediction scores is indicative of a predicted co-morbidity of pathologies of physiological systems, wherein the individuals defined by the computed cluster denote individuals clinically similar to the target individual in terms of predicted co-morbidity of the plurality of pathologies of the plurality of physiological systems, and computing an aggregation of medical parameter(s) of the cluster.

Abstract: A method of evaluating gastrointestinal cancer risk. The method comprises generating a set of features comprising a plurality of current blood test results from a blood collected from a target individual, providing at least one classifier generated according to an analysis of a plurality of respective historical blood test results of each of another of a plurality of sampled individuals, and evaluating, using a processor, a gastrointestinal cancer risk of the target individual by classifying the set of features using the at least one classifier.

Abstract: A method of evaluating gastrointestinal cancer risk. The method comprises generating a set of features comprising a plurality of current blood test results from a blood collected from a target individual, providing at least one classifier generated according to an analysis of a plurality of respective historical blood test results of each of another of a plurality of sampled individuals, and evaluating, using a processor, a gastrointestinal cancer risk of the target individual by classifying the set of features using the at least one classifier.

Abstract: There is provided a method of computing computed pathological indication(s), comprising: receiving an indication of values of current laboratory test results calculated based on an automated analysis of laboratory sample(s) collected from a target individual, selecting classifier(s) according to an analysis of the indication of values of the current laboratory test results, determining additional laboratory test(s) according to the analysis of the indication of values of the current laboratory test results and/or the selected classifier(s), generating instructions for automatic execution of second automatic laboratory testing device(s) on the laboratory sample(s) to obtain a second indication of a second value of the additional laboratory test(s), and evaluating computed pathological indication(s) by applying the selected classifier(s) to the indication of values of the current laboratory test results and the second indication of the second value of the additional laboratory test result(s).

Abstract: A method of evaluating gastrointestinal cancer risk. The method comprises generating a set of features comprising a plurality of current blood test results from a blood collected from a target individual, providing at least one classifier generated according to an analysis of a plurality of respective historical blood test results of each of another of a plurality of sampled individuals, and evaluating, using a processor, a gastrointestinal cancer risk of the target individual by classifying the set of features using the at least one classifier.

Abstract: A method of evaluating gastrointestinal cancer risk. The method comprises generating a set of features comprising a plurality of current blood test results from a blood collected from a target individual, providing at least one classifier generated according to an analysis of a plurality of respective historical blood test results of each of another of a plurality of sampled individuals, and evaluating, using a processor, a gastrointestinal cancer risk of the target individual by classifying the set of features using the at least one classifier.

Abstract: A method of evaluating lung cancer risk by generating a set of features comprising a plurality of current blood test results from a blood collected from a target individual, providing at least one classifier generated according to an analysis of a plurality of respective historical blood test results of each of another of a plurality of sampled individuals, and evaluating, using a processor, a lung cancer risk of said target individual by classifying said set of features using said at least one classifier. Each of said plurality of historical and current blood test results comprises results of one or more selected blood tests such as white blood cells blood test results, biochemistry test results, platelets blood test results including at least one of platelets count and mean platelet volume (MPV) and/or a combination of smoking history and red cell test results.

Abstract: A method of evaluating gastrointestinal cancer risk. The method comprises generating a set of features comprising a plurality of current blood test results from a blood collected from a target individual, providing at least one classifier generated according to an analysis of a plurality of respective historical blood test results of each of another of a plurality of sampled individuals, and evaluating, using a processor, a gastrointestinal cancer risk of the target individual by classifying the set of features using the at least one classifier.

Abstract: Previously compressed data is re-compressed with a better algorithm. A data analyzer analyses the compressed data to determine whether the data has been compressed with a supported compression format such as GIF, JPG or PNG. A data decompressor decompresses the data, then a data compressor re-compresses the data using the superior compression algorithm. When the data is to be reused, a data decompressor decompresses the data and a data compressor recompresses the data using the original compression algorithm. The invention may be implemented over a path in a data network. Compressed data that is to be sent over path is intercepted and re-compressed with the superior algorithm before being sent over the path. On the other end of the path, the data is converted back to its original compression format.

Abstract: According to the present invention, there is provided a method for compressing each of a plurality of data packets to form a compressed packet for transmission by a communication device, the data packets being composed of a sequence of data elements and the data packets being stored on a first computer such that the method is performed by the first computer, the method comprising the steps of: (a) receiving one of the plurality of data packets designated as packet P.sub.m ; (b) parsing the packet P.sub.m, such that the sequence of data elements of the packet P.sub.