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Chaitanya Shivade
Researcher at Amazon.com
Publications - 37
Citations - 1140
Chaitanya Shivade is an academic researcher from Amazon.com. The author has contributed to research in topics: Transfer of learning & Automatic summarization. The author has an hindex of 11, co-authored 36 publications receiving 859 citations. Previous affiliations of Chaitanya Shivade include University of Maryland, College Park & Ohio State University.
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Journal ArticleDOI
A review of approaches to identifying patient phenotype cohorts using electronic health records
Chaitanya Shivade,Preethi Raghavan,Eric Fosler-Lussier,Peter J. Embi,Noémie Elhadad,Stephen B. Johnson,Albert Lai +6 more
TL;DR: There are a variety of approaches for classifying patients into a particular phenotype, and good performance is reported on datasets at respective institutions, however, no system makes comprehensive use of electronic medical records addressing all of their known weaknesses.
Proceedings ArticleDOI
Lessons from Natural Language Inference in the Clinical Domain
Alexey Romanov,Chaitanya Shivade +1 more
TL;DR: This work introduces MedNLI - a dataset annotated by doctors, performing a natural language inference task (NLI), grounded in the medical history of patients, and presents strategies to leverage transfer learning using datasets from the open domain and incorporate domain knowledge from external data and lexical sources.
Proceedings ArticleDOI
Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering
TL;DR: The shared task is motivated by a need to develop relevant methods, techniques and gold standards for inference and entailment in the medical domain, and their application to improve domain specific information retrieval and question answering systems.
Proceedings ArticleDOI
Towards building large-scale distributed systems for twitter sentiment analysis
TL;DR: A large-scale distributed system for real-time Twitter sentiment analysis that is scalable with the number of machines and the size of data, and the accuracy of the sentiment classifier can be improved by combining the lexicon with a machine learning technique.
Journal ArticleDOI
Diagnosis-specific readmission risk prediction using electronic health data: a retrospective cohort study
Courtney Hebert,Chaitanya Shivade,Randi E. Foraker,Jared R. Wasserman,Jared R. Wasserman,Caryn Roth,Hagop S. Mekhjian,Stanley Lemeshow,Peter J. Embi +8 more
TL;DR: The readmission-risk models for acute myocardial infarction and pneumonia validated well on a contemporary cohort, but not as well on an historical cohort, suggesting that models such as these need to be continuously trained and adjusted to respond to local trends.