ArticleBMC bioinformatics2019
A question-entailment approach to question answering.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Question answering systems for health professionals at the point of care-a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2024Pooled it
- Structured taxonomy and framework for developing medical benchmark in large language models derived from scoping review.NPJ digital medicine · 2026Article
- Advanced natural-based interaction for the ITAlian language: LLaMAntino-3-ANITA.Scientific reports · 2026Article
- EvoMDT: a self-evolving multi-agent system for structured clinical decision-making in multi-cancer.NPJ digital medicine · 2026Article
- Overview of RAG-based and LLM-based approaches to personalization in healthcare AI applications.Frontiers in artificial intelligence · 2026Review
- Scaling Large Language Models for Next-Generation Single-Cell Analysis.bioRxiv : the preprint server for biology · 2026Article
- Does Domain-Specific Retrieval Augmented Generation Help LLMs Answer Consumer Health Questions?Proceedings of machine learning research · 2025Article
- Article
- Large language model trained on clinical oncology data predicts cancer progression.NPJ digital medicine · 2025Article
- Medical LLMs: Fine-Tuning vs. Retrieval-Augmented Generation.Bioengineering (Basel, Switzerland) · 2025Article
- The Venus score for the assessment of the quality and trustworthiness of biomedical datasets.BioData mining · 2025Article
- IDQuAD: Infectious disease question and answering dataset.PloS one · 2025Article
- Article
- Article
- Large language models in health care: Development, applications, and challenges.Health care science · 2023Review
- Automatic extraction of social determinants of health from medical notes of chronic lower back pain patients.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Classification of neurologic outcomes from medical notes using natural language processing.Expert systems with applications · 2023Article
- A large language model for electronic health records.NPJ digital medicine · 2022Article
- Automatic question answering for multiple stakeholders, the epidemic question answering dataset.Scientific data · 2022Article
- Question-aware transformer models for consumer health question summarization.Journal of biomedical informatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOne of the challenges in large-scale information retrieval (IR) is developing fine-grained and domain-specific methods to answer natural language questions. Despite the availability of numerous sources and datasets for answer retrieval, Question Answering (QA) remains a challenging problem due to the difficulty of the question understanding and answer extraction tasks. One of the promising tracks investigated in QA is mapping new questions to formerly answered questions that are "similar".
resultsWe propose a novel QA approach based on Recognizing Question Entailment (RQE) and we describe the QA system and resources that we built and evaluated on real medical questions. First, we compare logistic regression and deep learning methods for RQE using different kinds of datasets including textual inference, question similarity, and entailment in both the open and clinical domains. Second, we combine IR models with the best RQE method to select entailed questions and rank the retrieved answers. To study the end-to-end QA approach, we built the MedQuAD collection of 47,457 question-answer pairs from trusted medical sources which we introduce and share in the scope of this paper. Following the evaluation process used in TREC 2017 LiveQA, we find that our approach exceeds the best results of the medical task with a 29.8% increase over the best official score.
conclusionsThe evaluation results support the relevance of question entailment for QA and highlight the effectiveness of combining IR and RQE for future QA efforts. Our findings also show that relying on a restricted set of reliable answer sources can bring a substantial improvement in medical QA.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.