SEETrials: Leveraging large language models for safety and efficacy extraction in oncology clinical trials.

ABSTRACT:

Background

Initial insights into oncology clinical trial outcomes are often gleaned manually from conference abstracts. We aimed to develop an automated system to extract safety and efficacy information from study abstracts with high precision and fine granularity, transforming them into computable data for timely clinical decision-making.

Methods

We collected clinical trial abstracts from key conferences and PubMed (2012-2023). The SEETrials system was developed with three modules: preprocessing, prompt engineering with knowledge ingestion, and postprocessing. We evaluated the system's performance qualitatively and quantitatively and assessed its generalizability across different cancer types- multiple myeloma (MM), breast, lung, lymphoma, and leukemia. Furthermore, the eff

SUBMITTER: Lee K 

PROVIDER: S-EPMC11530223 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature