ArticleFindings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing2024
README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP.
Article in Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed.
- Article
- Chatbot To Help Patients Understand Their Health.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2025Article
- MedCOD: Enhancing English-to-Spanish Medical Translation of Large Language Models Using Enriched Chain-of-Dictionary Framework.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2025Article
- MedReadCtrl: Personalizing medical text generation with readability-controlled instruction learning.medRxiv : the preprint server for health sciences · 2025Article
- RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2025Article
- MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison Feedback.Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting · 2025Article
- Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
The advancement in healthcare has shifted focus toward patient-centric approaches, particularly in self-care and patient education, facilitated by access to Electronic Health Records (EHR). However, medical jargon in EHRs poses significant challenges in patient comprehension. To address this, we introduce a new task of automatically generating lay definitions, aiming to simplify complex medical terms into patient-friendly lay language. We first created the README dataset, an extensive collection of over 50,000 unique (medical term, lay definition) pairs and 300,000 mentions, each offering context-aware lay definitions manually annotated by domain experts. We have also engineered a data-centric Human-AI pipeline that synergizes data filtering, augmentation, and selection to improve data quality. We then used README as the training data for models and leveraged a Retrieval-Augmented Generation method to reduce hallucinations and improve the quality of model outputs. Our extensive automatic and human evaluations demonstrate that open-source mobile-friendly models, when fine-tuned with high-quality data, are capable of matching or even surpassing the performance of state-of-the-art closed-source large language models like ChatGPT. This research represents a significant stride in closing the knowledge gap in patient education and advancing patient-centric healthcare solutions .
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.