Development of Medical Databases using Large Language Models
- A database, PubMedMA (PubMed Meta-Analyses), was developed, which is based on the analysis of biomedical research papers and summaries using a Large Language Model (LLaMa3). The aim is to collect medical data in a structured database as medical records and make them usable for meta-analyses. By using Large Language Models, the analysis and extraction of data from the PubMed database for medical papers and summaries is implemented and automated as a pipeline. The generated medical records include information on diseases, drugs, medical groups and outcomes as well as results and are stored in the PubMedMA database. This enables comprehensive medical meta-analyses and promotes research through easy accessibility and structured query options that ensure clear access to the data. The evaluation of the results confirmed that the accuracy of data extraction and processing was generally good. The database proves to be a potentially valuable tool for medical researchers and the pharmaceutical industry to accelerate research and development processes and improve treatment methods.
| Author: | Niko Kauz, Peter SchanbacherORCiD |
|---|---|
| URL: | https://american-cse.org/static/Book-of-abstracts-updated.pdf |
| ISBN: | 1-60132-521-5 |
| Parent Title (German): | CSCI 2024 : 11th Annual Conference on Computational Science and Computational Intelligence, December 11 - 13, 2024, Las Vegas, USA, Book of Abstracts |
| Document Type: | Conference Proceeding |
| Language: | German |
| Year of Completion: | 2024 |
| Release Date: | 2024/12/18 |
| Tag: | Automated data extraction; Biomedical research; Large language models (LLM); Medical papers; Meta-analyses; Natural language processing (NLP); PubMed |
| Licence (German): |  Urheberrechtlich geschützt |