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.
翻译:本文件介绍澳大利亚研究理事会医疗技术认知计算工业改造培训中心自然语言处理小组向TREC 2021临床试验轨迹提交的呈文,重点是将符合资格的临床试验与构成患者入院笔记摘要的专题相匹配的问题。我们探索了使用NLP技术代表试验和专题的不同方式,然后使用共同检索模式生成每个专题的相关试验的排名清单。我们提交的所有试卷的结果都大大高于所有专题的中位分数,但仍有很多改进的余地。