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 for creating a personalized reference interval (PRI), comprising: receiving a digital profile of a patient including demographic parameters and associated values, and medical history data parameters and associated values; receiving a clinical test result including a measured value of at least one analyte; accessing a dataset storing digital profiles of other patients; identifying a subset of the dataset based a correlation according to a similarity requirement between demographic parameter(s) of the patient and of other patients, and between the medical history of the patient and the other patients; calculating a PRI for each respective analyte of the clinical test result of the patient according to an analysis of corresponding values of the analyte of the subset, wherein the PRI is dynamically calculated using the most updated version of the dataset.

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: A method of managing an allocation of medical examinations. The method comprises documenting, in at least one dataset, for each one of a plurality of individuals of a certain population, a plurality of historical medical test results, calculating, using a computerized processor, a plurality of relative scores each indicative of a risk of having a medical condition for one of the plurality of individuals based on respective the plurality of historical medical test results, each one of the plurality of relative scores is relative to the other of the plurality of relative scores, providing a population examination framework defining at least one criterion for selecting an individual, from the plurality of individuals, for a diagnosis of the medical condition, selecting a subgroup of the plurality of individuals according to the population examination framework, and designating members of the subgroup to perform the medical examinations.

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 for creating a personalized reference interval (PRI), comprising: receiving a digital profile of a patient including demographic parameters and associated values, and medical history data parameters and associated values; receiving a clinical test result including a measured value of at least one analyte; accessing a dataset storing digital profiles of other patients; identifying a subset of the dataset based a correlation according to a similarity requirement between demographic parameter(s) of the patient and of other patients, and between the medical history of the patient and the other patients; calculating a PRI for each respective analyte of the clinical test result of the patient according to an analysis of corresponding values of the analyte of the subset, wherein the PRI is dynamically calculated using the most updated version of the dataset.

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.