Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Shivade, Chaitanya"'
Autor:
He, Han, Liu, Qianchu, Xu, Lei, Shivade, Chaitanya, Zhang, Yi, Srinivasan, Sundararajan, Kirchhoff, Katrin
Existing automatic prompt engineering methods are typically designed for discriminative tasks, where new task prompts are iteratively refined with limited feedback from a single metric reflecting a single aspect. However, these approaches are subopti
Externí odkaz:
http://arxiv.org/abs/2410.02748
Medical coding is a complex task, requiring assignment of a subset of over 72,000 ICD codes to a patient's notes. Modern natural language processing approaches to these tasks have been challenged by the length of the input and size of the output spac
Externí odkaz:
http://arxiv.org/abs/2208.07444
Autor:
Shing, Han-Chin, Shivade, Chaitanya, Pourdamghani, Nima, Nan, Feng, Resnik, Philip, Oard, Douglas, Bhatia, Parminder
The records of a clinical encounter can be extensive and complex, thus placing a premium on tools that can extract and summarize relevant information. This paper introduces the task of generating discharge summaries for a clinical encounter. Summarie
Externí odkaz:
http://arxiv.org/abs/2104.13498
While there have been several contributions exploring state of the art techniques for text normalization, the problem of inverse text normalization (ITN) remains relatively unexplored. The best known approaches leverage finite state transducer (FST)
Externí odkaz:
http://arxiv.org/abs/2102.06380
Autor:
Kanjaria, Karina, Pillai, Anup, Shivade, Chaitanya, Bendersky, Marina, Jadhav, Ashutosh, Mukherjee, Vandana, Syeda-Mahmood, Tanveer
Publikováno v:
Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, ISBN 978-989-758-398-8, pages 178-186. 2020
Due to advances in machine learning and artificial intelligence (AI), a new role is emerging for machines as intelligent assistants to radiologists in their clinical workflows. But what systematic clinical thought processes are these machines using?
Externí odkaz:
http://arxiv.org/abs/2009.06082
In this working notes paper, we describe IBM Research AI (Almaden) team's participation in the ImageCLEF 2019 VQA-Med competition. The challenge consists of four question-answering tasks based on radiology images. The diversity of imaging modalities,
Externí odkaz:
http://arxiv.org/abs/1905.12008
Autor:
Melamud, Oren, Shivade, Chaitanya
Large-scale clinical data is invaluable to driving many computational scientific advances today. However, understandable concerns regarding patient privacy hinder the open dissemination of such data and give rise to suboptimal siloed research. De-ide
Externí odkaz:
http://arxiv.org/abs/1905.07002
Autor:
Shivade, Chaitanya P.
Clinical trials are instrumental in translating outcomes of scientific research into medical practice. Enrollment of patients that meet requirements of a significant sample size, within a desired time span, is limited by the speed and efficiency of s
Externí odkaz:
http://rave.ohiolink.edu/etdc/view?acc_num=osu1462810822
Autor:
Romanov, Alexey, Shivade, Chaitanya
State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs. However, they still lack generalization capabilities in conditions that differ from the ones encountered during training.
Externí odkaz:
http://arxiv.org/abs/1808.06752
We seek to address the lack of labeled data (and high cost of annotation) for textual entailment in some domains. To that end, we first create (for experimental purposes) an entailment dataset for the clinical domain, and a highly competitive supervi
Externí odkaz:
http://arxiv.org/abs/1606.02638