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Found 6 papers, 0 papers with code
RadQA: A Question Answering Dataset to Improve Comprehension of Radiology Reports
We present a radiology question answering dataset, RadQA, with 3074 questions posed against radiology reports and annotated with their corresponding answer spans (resulting in a total of 6148 question-answer evidence pairs) by physicians.
Ranked #1 on
Reading Comprehension
on RadQA
Toward a Neural Semantic Parsing System for EHR Question Answering
Thus, in this paper, we aim to systematically assess the performance of two such neural SP models for EHR question answering (QA).
Patient Cohort Retrieval using Transformer Language Models
Given the recent advancements in the field of document retrieval, we map the task of CR to a document retrieval task and apply various deep neural models implemented for the general domain tasks.
An Evaluation of Two Commercial Deep Learning-Based Information Retrieval Systems for COVID-19 Literature
This has led to both corpora for biomedical articles related to COVID-19 (such as the CORD-19 corpus (Wang et al., 2020)) as well as search engines to query such data.
Evaluation of Dataset Selection for Pre-Training and Fine-Tuning Transformer Language Models for Clinical Question Answering
We evaluate the performance of various Transformer language models, when pre-trained and fine-tuned on different combinations of open-domain, biomedical, and clinical corpora on two clinical question answering (QA) datasets (CliCR and emrQA).
A Paraphrase Generation System for EHR Question Answering
This paper proposes a dataset and method for automatically generating paraphrases for clinical questions relating to patient-specific information in electronic health records (EHRs).
