NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866915126549938176 |
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| author | Wang, Junda Yao, Zonghai Yang, Zhichao Zhou, Huixue Li, Rumeng Wang, Xun Xu, Yucheng Yu, Hong |
| author_facet | Wang, Junda Yao, Zonghai Yang, Zhichao Zhou, Huixue Li, Rumeng Wang, Xun Xu, Yucheng Yu, Hong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_15959 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes Wang, Junda Yao, Zonghai Yang, Zhichao Zhou, Huixue Li, Rumeng Wang, Xun Xu, Yucheng Yu, Hong Computation and Language 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. |
| title | NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2310.15959 |