CLIRMar 12, 2024

TMU at TREC Clinical Trials Track 2023

arXiv:2403.12088v14 citationsh-index: 3TREC
Originality Synthesis-oriented
AI Analysis

This is an incremental application of existing methods to a domain-specific task in clinical trial retrieval.

The paper tackled the problem of retrieving relevant clinical trials using advanced NLP techniques and neural language models, achieving results as part of the TREC Clinical Trials Track 2023 submission for Team V-TorontoMU, but no concrete numbers were provided.

This paper describes Toronto Metropolitan University's participation in the TREC Clinical Trials Track for 2023. As part of the tasks, we utilize advanced natural language processing techniques and neural language models in our experiments to retrieve the most relevant clinical trials. We illustrate the overall methodology, experimental settings, and results of our implementation for the run submission as part of Team - V-TorontoMU.

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