Rybinski, M., Kusa, W., Karimi, S., & Hanbury, A. (2024). Learning to match patients to clinical trials using large language models. Journal of Biomedical Informatics, 159, Article 104734. https://doi.org/10.1016/j.jbi.2024.104734
Humans; Information Storage and Retrieval; Semantics; Algorithms; Clinical trials; Information retrieval; Large language models; Learning-to-rank; Patient to trials matching; TCRR; TREC CT; Clinical Trials as Topic; Natural Language Processing; Patient Selection
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Abstract:
This study investigates the use of Large Language Models (LLMs) for matching patients to clinical trials (CTs) within an information retrieval pipeline. Our objective is to enhance the process of patient-trial matching by leveraging the semantic processing capabilities of LLMs, thereby improving the effectiveness of patient recruitment for clinical trials.
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Project title:
Domänen-spezifische Systeme für Informationsextraktion und -suche: 860721 (European Commission)
