Search Results for author: Chaitanya Shivade

Found 15 papers, 3 papers with code

Overview of the MEDIQA 2021 Shared Task on Summarization in the Medical Domain

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.

Text Summarization

Entity Anchored ICD Coding

no code implementations15 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.

Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes

no code implementations27 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.

Informativeness Sentence ReWriting

Neural Inverse Text Normalization

no code implementations12 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.

Receptivity of an AI Cognitive Assistant by the Radiology Community: A Report on Data Collected at RSNA

no code implementations13 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.

Multiple-choice Question Answering

Towards Visual Dialog for Radiology

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.

Question Answering Visual Dialog +2

Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering

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.

Information Retrieval Natural Language Inference +2

Leveraging Medical Visual Question Answering with Supporting Facts

no code implementations28 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.

Medical Visual Question Answering Multi-Task Learning +3

Lessons from Natural Language Inference in the Clinical Domain

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.

Natural Language Inference Transfer Learning

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