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: 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: 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 comprising receiving a dataset comprising: (i) a treatment plan for a subject, the treatment plan comprising a plurality of treatment events scheduled at specified intervals, and (ii) clinical outcomes of said subjects observed during said treatment plan; and automatically analyzing said dataset to determine adherence by said subject to said treatment plan.

Abstract: In an approach for constructing causal graphs, a processor receives data, a first set of constraints, and one or more graph parameters. A processor constructs a causal graph based on the data, first set of constraints, and one or more graph parameters. A processor generates an interactive display interface for the constructed causal graph. A processor refines the constructed causal graph using the interactive display interface.

Abstract: There is provided, in accordance with some embodiments, a method for receiving electronic documents representing a dependency graph comprising feature extractors at each graph node and directed edges corresponding to computational dependencies of the feature extractors. For at least some feature extractors, extractor defining data, comprising extractor data and computational dependencies of the graph node in the dependency graph are determined, and a node lookup key based on the extractor defining data is computed. When the node lookup key is associated with a stored set of output feature values, the stored set is assigned as output values of the feature extractor. When node lookup key is not associated with a stored set of output feature values, a new set of output feature values is computed, stored, and associated the node lookup key. The one set of output feature values are sent as an output feature set.

Abstract: Embodiments of the present systems and methods may provide generation of user and non-user cohorts for the estimation of drug's effect from longitudinal observational data that provide reduction of biases, account for progression of disease over time, and improve statistical significance. For example, in an embodiment, a computer-implemented method for conducting an observational trial of a drug under study may comprise receiving data relating to a plurality of patients, the data including drug prescription information for each patient, assigning each of the plurality of patients to a trial cohort based on the drug prescription information, setting an index date for each of the plurality of patients, and conducting an observational drug trial using the generated cohorts and index dates.

Abstract: Embodiments of the present invention disclose a method, a computer program product, and a computer system for decision support in long term therapy. A computer receives a pathogen drug resistance evolution model and retrieves population data. The computer then trains the drug resistance evolution model and identifies parameters corresponding to the drug resistance evolution model based on the retrieved population data. The computer then receives patient data and prescribes a therapy based on the drug resistance evolution model. In addition, the computer observes the results of the prescribed therapy and refines the drug resistance evolution model accordingly.

Abstract: In an approach for constructing causal graphs, a processor receives data, a first set of constraints, and one or more graph parameters. A processor constructs a causal graph based on the data, first set of constraints, and one or more graph parameters. A processor generates an interactive display interface for the constructed causal graph. A processor refines the constructed causal graph using the interactive display interface.

Abstract: There is provided, in accordance with some embodiments, a method for receiving electronic documents representing a dependency graph comprising feature extractors at each graph node and directed edges corresponding to computational dependencies of the feature extractors. For at least some feature extractors, extractor defining data, comprising extractor data and computational dependencies of the graph node in the dependency graph are determined, and a node lookup key based on the extractor defining data is computed. When the node lookup key is associated with a stored set of output feature values, the stored set is assigned as output values of the feature extractor. When node lookup key is not associated with a stored set of output feature values, a new set of output feature values is computed, stored, and associated the node lookup key. The one set of output feature values are sent as an output feature set.

Abstract: A method comprising using at least one hardware processor for: receiving (a) a proposition and (b) a plurality of claims; identifying a local claim polarity of each claim of the plurality of claims with respect to the proposition; calculating a pairwise claim polarity agreement score for each pair of claims of the pairs of claims reflecting the likelihood of said each pair of claims to have the same claim polarity, wherein the pairwise claim polarity agreement score is associated with each claim of the pair of claims; and determining a global claim polarity for each claim of the plurality of claims based on the local claim polarity of the claim and pairwise claim polarity agreement scores associated with said each claim.

Abstract: A method comprising using at least one hardware processor for: receiving (a) a proposition and (b) a plurality of claims; identifying a local claim polarity of each claim of the plurality of claims with respect to the proposition; calculating a pairwise claim polarity agreement score for each pair of claims of the pairs of claims reflecting the likelihood of said each pair of claims to have the same claim polarity, wherein the pairwise claim polarity agreement score is associated with each claim of the pair of claims; and determining a global claim polarity for each claim of the plurality of claims based on the local claim polarity of the claim and pairwise claim polarity agreement scores associated with said each claim.