International Business Machines Corporation

Kartikeya Vats

Mandar Mutalikdesai

Sheetal Srivastava

Ashish Rao

Sujoy Kumar Roy Chowdhury

Sandipto Neogi

Ria Chakraborty

Tanveer Akhter Khan

Sridhar Jonnala

Saigeetha Aswathnarayanan Jegannathan

Shivangi Tak

V Datta Kamesam Jami

Naman Mathur

Yogesh Narasimha

Chinthalapudi Venkata Sai Vishnu Vardhan

Debasish Kanhar

Rahul Reddy Nimmakayala

Khyati Baradia

Ojasvi Bhalerao

In an approach for automatically identifying one or more updates in a Scientific Drug Label (SL) relevant to a patient and incorporating the one or more updates into a Patient Drug Label (PL), a processor receives a pair of documents, wherein the pair of documents include the SL and the PL. A processor converts a complex medical language of the SL into a simplified patient friendly language. A processor identifies one or more words, one or more phrases, or one or more sentences that have been modified, inserted, or deleted. A processor searches for a location in the PL that closely maps to the one or more words, the one or more phrases, or the one or more sentences to the SL. A processor incorporates the one or more words, the one or more phrases, or the one or more sentences in a mapped location of the PL.

AI driven smart patient labeling system

A method and related system determine a localness measure that represents accessibility of a node connectiveness within a signed network. The method comprises, with a computer processor, receiving focus node (FN) information related to an FN of a graph representing the signed network, receiving neighbor node (NN) information related to a plurality of NNs in the signed network, and determining the localness measure based on the NN information and the FN information. The method and system also determine a harmony measure that represents importance of a node connectiveness within a signed network. The method comprises, with a computer processor, receiving source node (SN) information related to an SN of a graph representing the signed network, receiving destination node (DN) information related to a DN in the signed network, and determining the harmony measure based on the SN information and the DN information.

Global and local measures of centrality for signed and unsigned networks

A system and method for modeling preferences of customers to drive personalized contextual recommendations using cognitive deep constrained filtering includes receiving a user query on an online retail platform, in response to receiving the user query, performing a first online matrix manipulation and a second online matrix manipulation, and sending a list of ranked recommended products.

Deep cognitive constrained filtering for product recommendation

One or more computer processors creating a plurality of k-hop neighborhood contextual subgraphs utilizing extracted labelled nodes from an input graph; compute an eigenvector centrality score for each node contained in each created subgraph in the plurality of k-hop neighborhood contextual subgraphs; propagate a label for each node in each subgraph in the plurality of k-hop neighborhood contextual subgraphs leveraging an aggregated mathematical decay function, preserving a topical context of the label; calculate an attributable prestige vector for each node in each subgraph in the plurality of k-hop neighborhood contextual subgraphs based on the propagated label and the computed eigenvector centrality score associated with each node in each subgraph in the plurality of k-hop neighborhood contextual subgraph; and unsupervised predict a subsequent label for one or more subsequent nodes, subgraphs, or graphs utilizing the calculated attributable prestige vectors for each node in each subgraph.

Unsupervised contextual label propagation and scoring

Systems and methods are described for generating learning roadmaps from unstructured information. The systems and methods may provide for extracting a plurality of named entities from one or more corpora of information, constructing a graph based on the named entities, inducing a subgraph from the graph based on a target named entity, wherein the subgraph includes a subset of the named entities, ordering the subset of the named entities based on the subgraph, and generating a learning roadmap for the target named entity based on the ordering.

Learning roadmaps from unstructured text

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Karnataka, India

Kartikeya Vats