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Found 15 papers, 3 papers with code
Addressing Limited Data for Textual Entailment Across Domains
We seek to address the lack of labeled data (and high cost of annotation) for textual entailment in some domains.
Lessons from Natural Language Inference in the Clinical Domain
State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs.
Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language Models
Large-scale clinical data is invaluable to driving many computational scientific advances today.
Leveraging Medical Visual Question Answering with Supporting Facts
In this working notes paper, we describe IBM Research AI (Almaden) team's participation in the ImageCLEF 2019 VQA-Med competition.
Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering
MEDIQA 2019 includes three tasks: Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and Question Answering (QA) in the medical domain.
Towards Visual Dialog for Radiology
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.
Receptivity of an AI Cognitive Assistant by the Radiology Community: A Report on Data Collected at RSNA
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.
Neural Inverse Text Normalization
We propose an efficient and robust neural solution for ITN leveraging transformer based seq2seq models and FST-based text normalization techniques for data preparation.
Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes
The records of a clinical encounter can be extensive and complex, thus placing a premium on tools that can extract and summarize relevant information.
Entity Anchored ICD Coding
Medical coding is a complex task, requiring assignment of a subset of over 72, 000 ICD codes to a patient's notes.
Overview of the MEDIQA 2021 Shared Task on Summarization in the Medical Domain
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.
