Abstract: There is provided a method for generating instructions for adjusting a medical treatment of a patient, comprising: detecting an indication of a new symptom appearing in a patient being treated with medications for medical condition(s), computing a value indicative of risk of the new symptom being an adverse drug reaction (ADR) of one or a combination of the medications, detecting an indication of another new medication for treating the patient, computing likelihood of PC when the new medication is for treating the new symptom and the value is according to a requirement, generating a request for substitute medication(s) for the one or combination of medications when likelihood of PC is detected, and generating instructions for adjusting treatment of the patient by substituting the one or combination of medications with the substitute medication(s), and for terminating administration of the new medication or avoiding administration of the new medication.

Abstract: A method and system for cell tracking between spreadsheets. The method includes: assigning a unique first identifier to each cell containing data within a first spreadsheet; identifying characteristics of each cell containing data within the first spreadsheet and each cell containing data within at least one second spreadsheet, wherein the characteristics include data and metadata associated with each of the cells and wherein the first spreadsheet and the at least one second spreadsheet comprise at least a two-dimensional array of cells; analyzing the cells of the at least one second spreadsheet to compare each cell therein to the cells of the first spreadsheet, wherein the comparison is based on the identified characteristics of each cell, and wherein a comparison above a predetermined threshold is identified as a match; and assigning the unique first identifier of a cell of the first spreadsheet to a matching cell of the at least one second spreadsheet.

Abstract: Embodiments of the present systems and methods may provide techniques that provide enrichment of semantic graphing with relations that can enable a higher resolution of the semantic relationships and enable a more accurate prediction of new relations in the graph. For example a method for drug discovery and drug repositioning may comprise generating semantic relationships, at the computer system, based on data relating to a plurality of aspects of drugs and pharmaceutical compounds, generated semantic relationships represented in the form of a semantic graph, learning, at the computer system, new relations among the semantic relationships in the semantic graph using Denoising Autoencoders to process the semantic graph, and generating, at the computer system, predictions for drug discovery and drug repositioning based on the semantic relationships, including the newly found relations.

Abstract: There is provided, a method of selecting patients for treatment, comprising: feeding anatomical image(s) of a patient depicting a target tissue, and non-imaging clinical parameters of the patient into neural network component(s) of a model, outputting by the neural network component(s), an intermediate vector storing a plurality of embedding values computed for the anatomical image(s), a plurality of values outputted by a dense layer of the neural network component(s) in response to an input of at least some of the non-imaging clinical parameters, and an intermediate value indicative of likelihood of malignancy for the target tissue, feeding into a classifier component of the model, a feature vector created from the intermediate vector and the plurality of non-imaging clinical parameters, and selecting patients for treatment according to an indication of likelihood of malignancy in the target tissue outputted by the model.

Abstract: Embodiments of the present systems and methods may provide techniques that provide enrichment of semantic graphing with relations that can enable a higher resolution of the semantic relationships and enable a more accurate prediction of new relations in the graph. For example a method for drug discovery and drug repositioning may comprise generating semantic relationships, at the computer system, based on data relating to a plurality of aspects of drugs and pharmaceutical compounds, generated semantic relationships represented in the form of a semantic graph, learning, at the computer system, new relations among the semantic relationships in the semantic graph using Denoising Autoencoders to process the semantic graph, and generating, at the computer system, predictions for drug discovery and drug repositioning based on the semantic relationships, including the newly found relations.

Abstract: There is provided a method for generating instructions for adjusting a medical treatment of a patient, comprising: detecting an indication of a new symptom appearing in a patient being treated with medications for medical condition(s), computing a value indicative of risk of the new symptom being an adverse drug reaction (ADR) of one or a combination of the medications, detecting an indication of another new medication for treating the patient, computing likelihood of PC when the new medication is for treating the new symptom and the value is according to a requirement, generating a request for substitute medication(s) for the one or combination of medications when likelihood of PC is detected, and generating instructions for adjusting treatment of the patient by substituting the one or combination of medications with the substitute medication(s), and for terminating administration of the new medication or avoiding administration of the new medication.

Abstract: A method and system for logical comparison of different versions of a spreadsheet are provided. The method includes identifying, in each of a first version and a second version of the spreadsheet, data and metadata associated with their respective headers; comparing between the first version and the second version of the spreadsheet to identify at least one structural difference, wherein the comparison is based on the identified headers data and metadata; comparing between the first version and the second version of the spreadsheet to identify at least one content value difference, wherein the comparison is based, in part, on the at least one identified structural difference; and saving the at least one identified structural difference and the at least one identified content value difference in a differences (Diff) dataset.

Abstract: A method and system for cell tracking between spreadsheets. The method includes: assigning a unique first identifier to each cell containing data within a first spreadsheet; identifying characteristics of each cell containing data within the first spreadsheet and each cell containing data within at least one second spreadsheet, wherein the characteristics include data and metadata associated with each of the cells and wherein the first spreadsheet and the at least one second spreadsheet comprise at least a two-dimensional array of cells; analyzing the cells of the at least one second spreadsheet to compare each cell therein to the cells of the first spreadsheet, wherein the comparison is based on the identified characteristics of each cell, and wherein a comparison above a predetermined threshold is identified as a match; and assigning the unique first identifier of a cell of the first spreadsheet to a matching cell of the at least one second spreadsheet.

Abstract: A method and system for logical comparison of different versions of a spreadsheet are provided. The method includes identifying, in each of a first version and a second version of the spreadsheet, data and metadata associated with their respective headers; comparing between the first version and the second version of the spreadsheet to identify at least one structural difference, wherein the comparison is based on the identified headers data and metadata; comparing between the first version and the second version of the spreadsheet to identify at least one content value difference, wherein the comparison is based, in part, on the at least one identified structural difference; and saving the at least one identified structural difference and the at least one identified content value difference in a differences (Diff) dataset.

Abstract: A method for deriving accurate body size measures of a user from a sequence of 2D images includes: a) automatically guiding the user through a sequence of body poses and motions; b) scanning the body of said user by obtaining a sequence of raw 2D images of said user as captured by at least one camera during said guided sequence of poses and motions; c) analyzing the behavior of said user to ensure that the user follows the provided instructions; d) extracting and encoding 2D shape data descriptors from said sequence of images by using a 2D shape analyzer (2DSA); and e) integrating said 2D shape descriptors and data representing the user's position, pose and rotation into a 3D shape model.