ITTC @ TREC 2021 Clinical Trials Track
This work addresses a domain-specific problem in medical informatics for clinical trial matching, but it is incremental as it applies existing methods to a new dataset.
The paper tackled matching eligible clinical trials to patient admission notes using NLP techniques for representation and a retrieval model, achieving results above median scores but with room for improvement.
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.