Extracting Epilepsy Patient Data with Llama 2
Ben Holgate, Shichao Fang, Anthony Shek, Matthew McWilliam, Pedro Viana, Joel S. Winston, James T. Teo, Mark P. Richardson
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.- Anthology ID:
- 2024.bionlp-1.43
- Volume:
- Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Dina Demner-Fushman, Sophia Ananiadou, Makoto Miwa, Kirk Roberts, Junichi Tsujii
- Venues:
- BioNLP | WS
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 526–535
- Language:
- URL:
- https://aclanthology.org/2024.bionlp-1.43
- DOI:
- Cite (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.
- Cite (Informal):
- Extracting Epilepsy Patient Data with Llama 2 (Holgate et al., BioNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.bionlp-1.43.pdf