README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP

• URL: http://arxiv.org/abs/2312.15561v5
• Date: Fri, 25 Oct 2024 14:30:28 GMT
• Title: README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP
• Authors: Zonghai Yao, Nandyala Siddharth Kantu, Guanghao Wei, Hieu Tran, Zhangqi Duan, Sunjae Kwon, Zhichao Yang, README annotation team, Hong Yu,
• Abstract summary: We introduce a new task of automatically generating lay definitions, aiming to simplify medical terms into patient-friendly lay language. We first created the dataset, an extensive collection of over 50,000 unique (medical term, lay definition) pairs and 300,000 mentions. We have also engineered a data-centric Human-AI pipeline that synergizes data filtering, augmentation, and selection to improve data quality.
• Score: 9.432205523734707
• License:
• 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.

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