@inproceedings{holgate-etal-2025-fine,
title = "Fine-tuning {LLM}s to Extract Epilepsy Seizure Frequency Data from Health Records",
author = "Holgate, Ben and
Davies, Joe and
Fang, Shichao and
Winston, Joel and
Teo, James and
Richardson, Mark",
editor = "Demner-Fushman, Dina and
Ananiadou, Sophia and
Miwa, Makoto and
Tsujii, Junichi",
booktitle = "Proceedings of the 24th Workshop on Biomedical Language Processing",
month = aug,
year = "2025",
address = "Viena, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.bionlp-1.5/",
doi = "10.18653/v1/2025.bionlp-1.5",
pages = "44--55",
ISBN = "979-8-89176-275-6",
abstract = "We developed a new methodology of extracting the frequency of a patient{'}s epilepsy seizures from unstructured, free-text outpatient clinic letters by: first, devising a singular unit of measurement for seizure frequency; and second, fine-tuning a generative Large Language Model (LLM) on our bespoke annotated dataset. We measured frequency by the number of seizures per month: one seizure or more requires an integer; and less than one a decimal. This approach enables us to track whether a patient{''}s seizures are improving or not over time. We found fine-tuning improves the F1 score of our best-performing LLM, Ministral-8B-Instruct-2410, by around three times compared to an untrained model. We also found Ministral demonstrated an impressive ability for mathematical reasoning."
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<abstract>We developed a new methodology of extracting the frequency of a patient’s epilepsy seizures from unstructured, free-text outpatient clinic letters by: first, devising a singular unit of measurement for seizure frequency; and second, fine-tuning a generative Large Language Model (LLM) on our bespoke annotated dataset. We measured frequency by the number of seizures per month: one seizure or more requires an integer; and less than one a decimal. This approach enables us to track whether a patient”s seizures are improving or not over time. We found fine-tuning improves the F1 score of our best-performing LLM, Ministral-8B-Instruct-2410, by around three times compared to an untrained model. We also found Ministral demonstrated an impressive ability for mathematical reasoning.</abstract>
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%0 Conference Proceedings
%T Fine-tuning LLMs to Extract Epilepsy Seizure Frequency Data from Health Records
%A Holgate, Ben
%A Davies, Joe
%A Fang, Shichao
%A Winston, Joel
%A Teo, James
%A Richardson, Mark
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Miwa, Makoto
%Y Tsujii, Junichi
%S Proceedings of the 24th Workshop on Biomedical Language Processing
%D 2025
%8 August
%I Association for Computational Linguistics
%C Viena, Austria
%@ 979-8-89176-275-6
%F holgate-etal-2025-fine
%X We developed a new methodology of extracting the frequency of a patient’s epilepsy seizures from unstructured, free-text outpatient clinic letters by: first, devising a singular unit of measurement for seizure frequency; and second, fine-tuning a generative Large Language Model (LLM) on our bespoke annotated dataset. We measured frequency by the number of seizures per month: one seizure or more requires an integer; and less than one a decimal. This approach enables us to track whether a patient”s seizures are improving or not over time. We found fine-tuning improves the F1 score of our best-performing LLM, Ministral-8B-Instruct-2410, by around three times compared to an untrained model. We also found Ministral demonstrated an impressive ability for mathematical reasoning.
%R 10.18653/v1/2025.bionlp-1.5
%U https://aclanthology.org/2025.bionlp-1.5/
%U https://doi.org/10.18653/v1/2025.bionlp-1.5
%P 44-55
Markdown (Informal)
[Fine-tuning LLMs to Extract Epilepsy Seizure Frequency Data from Health Records](https://aclanthology.org/2025.bionlp-1.5/) (Holgate et al., BioNLP 2025)
ACL