no code implementations • NAACL (BioNLP) 2021 • Asma Ben Abacha, Yassine Mrabet, Yuhao Zhang, Chaitanya Shivade, Curtis Langlotz, Dina Demner-Fushman
The MEDIQA 2021 shared tasks at the BioNLP 2021 workshop addressed three tasks on summarization for medical text: (i) a question summarization task aimed at exploring new approaches to understanding complex real-world consumer health queries, (ii) a multi-answer summarization task that targeted aggregation of multiple relevant answers to a biomedical question into one concise and relevant answer, and (iii) a radiology report summarization task addressing the development of clinically relevant impressions from radiology report findings.
no code implementations • 15 Aug 2022 • Jay DeYoung, Han-Chin Shing, Luyang Kong, Christopher Winestock, Chaitanya Shivade
Medical coding is a complex task, requiring assignment of a subset of over 72, 000 ICD codes to a patient's notes.
no code implementations • 27 Apr 2021 • Han-Chin Shing, Chaitanya Shivade, Nima Pourdamghani, Feng Nan, Philip Resnik, Douglas Oard, Parminder Bhatia
The records of a clinical encounter can be extensive and complex, thus placing a premium on tools that can extract and summarize relevant information.
no code implementations • 12 Feb 2021 • Monica Sunkara, Chaitanya Shivade, Sravan Bodapati, Katrin Kirchhoff
We propose an efficient and robust neural solution for ITN leveraging transformer based seq2seq models and FST-based text normalization techniques for data preparation.
no code implementations • 13 Sep 2020 • Karina Kanjaria, Anup Pillai, Chaitanya Shivade, Marina Bendersky, Ashutosh Jadhav, Vandana Mukherjee, Tanveer Syeda-Mahmood
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.
no code implementations • WS 2020 • Olga Kovaleva, Chaitanya Shivade, Satyan Kashyap, a, Karina Kanjaria, Joy Wu, Deddeh Ballah, Adam Coy, Alex Karargyris, ros, Yufan Guo, David Beymer Beymer, Anna Rumshisky, V Mukherjee, ana Mukherjee
Using MIMIC-CXR, an openly available database of chest X-ray images, we construct both a synthetic and a real-world dataset and provide baseline scores achieved by state-of-the-art models.
1 code implementation • WS 2019 • Asma Ben Abacha, Chaitanya Shivade, Dina Demner-Fushman
MEDIQA 2019 includes three tasks: Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and Question Answering (QA) in the medical domain.
no code implementations • 28 May 2019 • Tomasz Kornuta, Deepta Rajan, Chaitanya Shivade, Alexis Asseman, Ahmet S. Ozcan
In this working notes paper, we describe IBM Research AI (Almaden) team's participation in the ImageCLEF 2019 VQA-Med competition.
1 code implementation • WS 2019 • Oren Melamud, Chaitanya Shivade
Large-scale clinical data is invaluable to driving many computational scientific advances today.
3 code implementations • EMNLP 2018 • Alexey Romanov, Chaitanya Shivade
State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs.
no code implementations • ACL 2016 • Chaitanya Shivade, Preethi Raghavan, Siddharth Patwardhan
We seek to address the lack of labeled data (and high cost of annotation) for textual entailment in some domains.