mlsanityData to 7 Oct 2026

TMU at TREC Clinical Trials Track 2023

arXiv 2403.12088 ↗First posted 12 Mar 2024cs.CL cs.IRcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 5 of its 6 references that are in the map. First posted on arXiv 12 Mar 2024.

Builds on

5 of 6 references.

References · 5

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  3. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  4. 2014Distributed Representations of Sentences and Documentscited by 677
  5. 2013A Theoretical Analysis of NDCG Type Ranking Measurescited by 119

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Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024AlzheimerRAG: Multimodal Retrieval Augmented Generation for Clinical Use Cases using PubMed articlescited by 3
  2. 2025A Survey on LLM-Assisted Clinical Trial Recruitmentcited by 2