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Extractive Clinical Question-Answering With Multianswer and Multifocus Questions: Data Set Development and Evaluation Study
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1038/s41598-021-98579-4openalex-snapshot
An updated, computable MEDication-Indication resource for biomedical research
inferredcites →https://doi.org/10.1093/bioinformatics/btac397openalex-snapshot
Sequence tagging for biomedical extractive question answering
inferredcites →https://doi.org/10.1093/jamia/ocz125openalex-snapshot
Putting the “why” in “EHR”: capturing and coding clinical cognition
inferredcites →https://doi.org/10.1093/jamia/ocz166openalex-snapshot
2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records
inferredcites →https://doi.org/10.1109/hdis56859.2022.9991478openalex-snapshot
A Survey of Extractive Question Answering
inferredcites →https://doi.org/10.1136/bmj.319.7206.358openalex-snapshot
Analysis of questions asked by family doctors regarding patient care
inferredcites →https://doi.org/10.1145/2983323.2983819openalex-snapshot
Medical Question Answering for Clinical Decision Support
inferredcites →https://doi.org/10.1145/3490238openalex-snapshot
Biomedical Question Answering: A Survey of Approaches and Challenges
inferredcites →https://doi.org/10.18653/v1/2020.acl-main.410openalex-snapshot
Clinical Reading Comprehension: A Thorough Analysis of the emrQA Dataset
inferredcites →https://doi.org/10.18653/v1/2020.clinicalnlp-1.13openalex-snapshot
How You Ask Matters: The Effect of Paraphrastic Questions to BERT Performance on a Clinical SQuAD Dataset
inferredcites →https://doi.org/10.18653/v1/2020.emnlp-main.248openalex-snapshot
A Simple and Effective Model for Answering Multi-span Questions
inferredcites →https://doi.org/10.18653/v1/2020.findings-emnlp.342openalex-snapshot
Question Answering with Long Multiple-Span Answers
inferredcites →https://doi.org/10.18653/v1/d16-1264openalex-snapshot
SQuAD: 100,000+ Questions for Machine Comprehension of Text
inferredcites →https://doi.org/10.18653/v1/d18-1258openalex-snapshot
emrQA: A Large Corpus for Question Answering on Electronic Medical Records
inferredcites →https://doi.org/10.18653/v1/d19-1170openalex-snapshot
A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning
inferredcites →https://doi.org/10.18653/v1/p18-2124openalex-snapshot
Know What You Don’t Know: Unanswerable Questions for SQuAD
inferredcites →https://doi.org/10.18653/v1/w19-5003openalex-snapshot
A Paraphrase Generation System for EHR Question Answering
inferredcites →https://doi.org/10.4230/lipics.cosit.2022.18openalex-snapshot
AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale
inferredcites →https://doi.org/10.48550/arxiv.1805.06816openalex-snapshot
Annotating Electronic Medical Records for Question Answering
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s10115-025-02551-xopenalex-snapshot
Chatting with organisational data: a generative AI approach applied to scientific reports for information seeking
inferred← citeshttps://doi.org/10.1016/j.esmorw.2026.100684openalex-snapshot
AI assistance in tumor multidisciplinary teams
inferred← citeshttps://doi.org/10.1016/j.procs.2026.05.168openalex-snapshot
MedXplainQA: Context-Aware Chest X-Ray Reporting with Question Answering Support
inferred← citeshttps://doi.org/10.1109/acmlc70381.2026.11700484openalex-snapshot
Adaptive Explainable Segmentation of Biomedical Questions for Knowledge Graph-Based Clinical Information Retrieval
inferred← citeshttps://doi.org/10.1109/i-smac58438.2023.10290240openalex-snapshot
Question Answering on Biomedical Research Papers using Transfer Learning on BERT-Base Models
inferred← citeshttps://doi.org/10.2196/53636openalex-snapshot
Question Answering for Electronic Health Records: Scoping Review of Datasets and Models
inferred← citeshttps://doi.org/10.30812/matrik.v25i2.5847openalex-snapshot
Comparative Analysis of Indonesian Pre-trained BERT Models for the Extractive Question Answering Task on an Indonesian-Translated SQuAD Dataset
inferred← citeshttps://doi.org/10.64898/2026.02.06.704502openalex-snapshot
Verifying LLM-extracted text with token alignment
inferred← citeshttps://doi.org/10.7287/peerj-cs.1725v0.1/reviews/1openalex-snapshot
Peer Review #1 of "Beyond top-k: knowledge reasoning for multi-answer temporal questions based on revalidation framework (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.1725v0.1/reviews/2openalex-snapshot
Peer Review #2 of "Beyond top-k: knowledge reasoning for multi-answer temporal questions based on revalidation framework (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.1725v0.2/reviews/1openalex-snapshot
Peer Review #1 of "Beyond top-k: knowledge reasoning for multi-answer temporal questions based on revalidation framework (v0.2)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.1725v0.2/reviews/2openalex-snapshot
Peer Review #2 of "Beyond top-k: knowledge reasoning for multi-answer temporal questions based on revalidation framework (v0.2)"
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