V
Vivek Khetan
Researcher at Accenture
Publications - 13
Citations - 217
Vivek Khetan is an academic researcher from Accenture. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 4, co-authored 9 publications receiving 153 citations. Previous affiliations of Vivek Khetan include University of Texas at Austin.
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Knowledge Graph Anchored Information-Extraction for Domain-Specific Insights.
Vivek Khetan,K. M. Annervaz,Erin B. Wetherley,Elena Eneva,Shubhashis Sengupta,Andrew E. Fano +5 more
TL;DR: In this article, a task-based approach for fulfilling specific information needs within a new domain is proposed to extract taskbased information from incoming instance data, where a pipeline constructed of state of the art NLP technologies, including a bi-LSTM-CRF model for entity extraction, attention-based deep semantic role labeling, and an automated verb-based relationship extractor, is used to automatically extract an instance level semantic structure.
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Identifying causal associations in tweets using deep learning: Use case on diabetes-related tweets from 2017-2021.
Adrian Ahne,Vivek Khetan,Xavier Tannier,Imbesat Hassan Rizvi,Thomas Czernichow,Francisco Orchard,Charline Bour,Andrew E. Fano,Guy Fagherazzi +8 more
TL;DR: In this article, a novel methodology was developed to detect causal sentences and identify both explicit and implicit, single and multi-word cause and corresponding effect as expressed in diabetes-related tweets leveraging BERT-based architectures and visualised as cause-effect-network.
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MIMICause : Defining, identifying and predicting types of causal relationships between biomedical concepts from clinical notes.
Vivek Khetan,Imbesat Hassan Rizvi,Jessica Huber,Paige Bartusiak,Bogdan Sacaleanu,Andrew E. Fano +5 more
TL;DR: In this paper, the authors propose annotation guidelines, develop an annotated corpus and provide baseline scores to identify types and direction of causal relations between a pair of biomedical concepts in clinical notes; communicated implicitly or explicitly, identified either in a single sentence or across multiple sentences.