ITTC @ TREC 2021 Clinical Trials Track
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Abstract (arXiv)
This paper describes the submissions of the Natural Language Processing (NLP) team from the Australian Research Council Industrial Transformation Training Centre (ITTC) for Cognitive Computing in Medical Technologies to the TREC 2021 Clinical Trials Track. The task focuses on the problem of matching eligible clinical trials to topics constituting a summary of a patient's admission notes. We explore different ways of representing trials and topics using NLP techniques, and then use a common retrieval model to generate the ranked list of relevant trials for each topic. The results from all our submitted runs are well above the median scores for all topics, but there is still plenty of scope for improvement.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 17 references that are in the map. First posted on arXiv 16 Feb 2022.
Builds on
6 of 17 references.
References · 6
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2018One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrasescited by 52
- 2019NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolutioncited by 36
- 2020An Empirical Study on Neural Keyphrase Generationcited by 25
- 2020Information Extraction of Clinical Trial Eligibility Criteriacited by 6
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Citing papers per year: 2022: 1.
1 paper.