README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866916452751114240 |
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| author | Yao, Zonghai Kantu, Nandyala Siddharth Wei, Guanghao Tran, Hieu Duan, Zhangqi Kwon, Sunjae Yang, Zhichao team, README annotation Yu, Hong |
| author_facet | Yao, Zonghai Kantu, Nandyala Siddharth Wei, Guanghao Tran, Hieu Duan, Zhangqi Kwon, Sunjae Yang, Zhichao team, README annotation Yu, Hong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_15561 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP Yao, Zonghai Kantu, Nandyala Siddharth Wei, Guanghao Tran, Hieu Duan, Zhangqi Kwon, Sunjae Yang, Zhichao team, README annotation Yu, Hong Computation and Language Artificial Intelligence 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. |
| title | README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2312.15561 |