International Business Machines Corporation

Chen Yanover

Adam Spiro

Ehud Aharoni

Noam Slonim

Ranit Aharonov

Indrajit Bhattacharya

Amrita Saha

Roy N Bar-Haim

Dan Gutfreund

Yaara Goldschmidt

Lavi Shpigelman

Omer Weissbrod

Michal Ozery-Flato

Francesco Dinuzzo

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.

Systems and methods for adjusting medical treatment to reduce likelihood of prescription cascade

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.

Automatic validation and enrichment of semantic relations between medical entities for drug discovery

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.

Interactive tool for causal graph construction

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.

Modular memoization, tracking and train-data management of feature extraction

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.

Claim polarity identification

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Zichron Yaakov, Israel

Chen Yanover