@inproceedings{holgate-etal-2024-extracting,
title = "Extracting Epilepsy Patient Data with Llama 2",
author = "Holgate, Ben and
Fang, Shichao and
Shek, Anthony and
McWilliam, Matthew and
Viana, Pedro and
Winston, Joel S. and
Teo, James T. and
Richardson, Mark P.",
editor = "Demner-Fushman, Dina and
Ananiadou, Sophia and
Miwa, Makoto and
Roberts, Kirk and
Tsujii, Junichi",
booktitle = "Proceedings of the 23rd Workshop on Biomedical Natural Language Processing",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.bionlp-1.43",
doi = "10.18653/v1/2024.bionlp-1.43",
pages = "526--535",
abstract = "We fill a gap in scholarship by applying a generative Large Language Model (LLM) to extract information from clinical free text about the frequency of seizures experienced by people with epilepsy. Seizure frequency is difficult to determine across time from unstructured doctors{'} and nurses{'} reports of outpatients{'} visits that are stored in Electronic Health Records (EHRs) in the United Kingdom{'}s National Health Service (NHS). We employ Meta{'}s Llama 2 to mine the EHRs of people with epilepsy and determine, where possible, a person{'}s seizure frequency at a given point in time. The results demonstrate that the new, powerful generative LLMs may improve outcomes for clinical NLP research in epilepsy and other areas.",
}
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%0 Conference Proceedings
%T Extracting Epilepsy Patient Data with Llama 2
%A Holgate, Ben
%A Fang, Shichao
%A Shek, Anthony
%A McWilliam, Matthew
%A Viana, Pedro
%A Winston, Joel S.
%A Teo, James T.
%A Richardson, Mark P.
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Miwa, Makoto
%Y Roberts, Kirk
%Y Tsujii, Junichi
%S Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F holgate-etal-2024-extracting
%X We fill a gap in scholarship by applying a generative Large Language Model (LLM) to extract information from clinical free text about the frequency of seizures experienced by people with epilepsy. Seizure frequency is difficult to determine across time from unstructured doctors’ and nurses’ reports of outpatients’ visits that are stored in Electronic Health Records (EHRs) in the United Kingdom’s National Health Service (NHS). We employ Meta’s Llama 2 to mine the EHRs of people with epilepsy and determine, where possible, a person’s seizure frequency at a given point in time. The results demonstrate that the new, powerful generative LLMs may improve outcomes for clinical NLP research in epilepsy and other areas.
%R 10.18653/v1/2024.bionlp-1.43
%U https://aclanthology.org/2024.bionlp-1.43
%U https://doi.org/10.18653/v1/2024.bionlp-1.43
%P 526-535
Markdown (Informal)
[Extracting Epilepsy Patient Data with Llama 2](https://aclanthology.org/2024.bionlp-1.43) (Holgate et al., BioNLP-WS 2024)
ACL
- Ben Holgate, Shichao Fang, Anthony Shek, Matthew McWilliam, Pedro Viana, Joel S. Winston, James T. Teo, and Mark P. Richardson. 2024. Extracting Epilepsy Patient Data with Llama 2. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 526–535, Bangkok, Thailand. Association for Computational Linguistics.