@inproceedings{giorgi-etal-2023-wanglab,
title = "{W}ang{L}ab at {MEDIQA}-Chat 2023: Clinical Note Generation from Doctor-Patient Conversations using Large Language Models",
author = "Giorgi, John and
Toma, Augustin and
Xie, Ronald and
Chen, Sondra and
An, Kevin and
Zheng, Grace and
Wang, Bo",
editor = "Naumann, Tristan and
Ben Abacha, Asma and
Bethard, Steven and
Roberts, Kirk and
Rumshisky, Anna",
booktitle = "Proceedings of the 5th Clinical Natural Language Processing Workshop",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.clinicalnlp-1.36",
doi = "10.18653/v1/2023.clinicalnlp-1.36",
pages = "323--334",
abstract = "This paper describes our submission to the MEDIQA-Chat 2023 shared task for automatic clinical note generation from doctor-patient conversations. We report results for two approaches: the first fine-tunes a pre-trained language model (PLM) on the shared task data, and the second uses few-shot in-context learning (ICL) with a large language model (LLM). Both achieve high performance as measured by automatic metrics (e.g. ROUGE, BERTScore) and ranked second and first, respectively, of all submissions to the shared task. Expert human scrutiny indicates that notes generated via the ICL-based approach with GPT-4 are preferred about as often as human-written notes, making it a promising path toward automated note generation from doctor-patient conversations.",
}
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%0 Conference Proceedings
%T WangLab at MEDIQA-Chat 2023: Clinical Note Generation from Doctor-Patient Conversations using Large Language Models
%A Giorgi, John
%A Toma, Augustin
%A Xie, Ronald
%A Chen, Sondra
%A An, Kevin
%A Zheng, Grace
%A Wang, Bo
%Y Naumann, Tristan
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Rumshisky, Anna
%S Proceedings of the 5th Clinical Natural Language Processing Workshop
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F giorgi-etal-2023-wanglab
%X This paper describes our submission to the MEDIQA-Chat 2023 shared task for automatic clinical note generation from doctor-patient conversations. We report results for two approaches: the first fine-tunes a pre-trained language model (PLM) on the shared task data, and the second uses few-shot in-context learning (ICL) with a large language model (LLM). Both achieve high performance as measured by automatic metrics (e.g. ROUGE, BERTScore) and ranked second and first, respectively, of all submissions to the shared task. Expert human scrutiny indicates that notes generated via the ICL-based approach with GPT-4 are preferred about as often as human-written notes, making it a promising path toward automated note generation from doctor-patient conversations.
%R 10.18653/v1/2023.clinicalnlp-1.36
%U https://aclanthology.org/2023.clinicalnlp-1.36
%U https://doi.org/10.18653/v1/2023.clinicalnlp-1.36
%P 323-334
Markdown (Informal)
[WangLab at MEDIQA-Chat 2023: Clinical Note Generation from Doctor-Patient Conversations using Large Language Models](https://aclanthology.org/2023.clinicalnlp-1.36) (Giorgi et al., ClinicalNLP 2023)
ACL