Huixue Zhou
2024
NoteChat: A Dataset of Synthetic Patient-Physician Conversations Conditioned on Clinical Notes
Junda Wang
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Zonghai Yao
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Zhichao Yang
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Huixue Zhou
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Rumeng Li
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Xun Wang
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Yucheng Xu
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Hong Yu
Findings of the Association for Computational Linguistics ACL 2024
We introduce NoteChat, a novel cooperative multi-agent framework leveraging Large Language Models (LLMs) to generate patient-physician dialogues. NoteChat embodies the principle that an ensemble of role-specific LLMs, through structured role-play and strategic prompting, can perform their assigned roles more effectively. The synergy among these role-playing LLMs results in a cohesive and efficient dialogue generation. Evaluation on MTS-dialogue, a benchmark dataset for patient-physician dialogues-note pairs, shows that models trained with the augmented synthetic patient-physician dialogues by NoteChat outperforms other state-of-the-art models for generating clinical notes. Our comprehensive automatic and human evaluation demonstrates that NoteChat substantially surpasses state-of-the-art models like ChatGPT and GPT-4 up to 22.78% by domain experts in generating superior synthetic patient-physician dialogues based on clinical notes. NoteChat has the potential to engage patients directly and help clinical documentation, a leading cause of physician burnout.
2023
PaniniQA: Enhancing Patient Education Through Interactive Question Answering
Pengshan Cai
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Zonghai Yao
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Fei Liu
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Dakuo Wang
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Meghan Reilly
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Huixue Zhou
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Lingxi Li
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Yi Cao
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Alok Kapoor
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Adarsha Bajracharya
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Dan Berlowitz
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Hong Yu
Transactions of the Association for Computational Linguistics, Volume 11
A patient portal allows discharged patients to access their personalized discharge instructions in electronic health records (EHRs). However, many patients have difficulty understanding or memorizing their discharge instructions (Zhao et al., 2017). In this paper, we present PaniniQA, a patient-centric interactive question answering system designed to help patients understand their discharge instructions. PaniniQA first identifies important clinical content from patients’ discharge instructions and then formulates patient-specific educational questions. In addition, PaniniQA is also equipped with answer verification functionality to provide timely feedback to correct patients’ misunderstandings. Our comprehensive automatic & human evaluation results demonstrate our PaniniQA is capable of improving patients’ mastery of their medical instructions through effective interactions.1
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Co-authors
- Zonghai Yao 2
- Hong Yu 2
- Junda Wang 1
- Zhichao Yang 1
- Rumeng Li 1
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