一种为系统评价更新推荐新试验证据的共享潜在空间矩阵分解方法
A shared latent space matrix factorisation method for recommending new trial evidence for systematic review updates
摘要：
临床试验登记可用于监测系统评价过时时试验证据和信号的产生。然而，由于搜索和筛选相关试验注册需要大量的人工审查，因此这种使用迄今为止受到限制。我们的目的是评估一种新方法，该方法可以部分自动识别可能与系统评价更新相关的试验注册。我们确定了179篇2型糖尿病药物干预的系统性综述，其中包括537项在ClinicalTrials.gov注册的临床试验。我们测试了一种矩阵因子分解方法，该方法使用共享潜在空间来学习如何对每个系统性综述的相关试验注册进行排名，比较性能与文档相似性，以对相关试验注册进行排名。这两种方法在来自2型糖尿病系统性综述的一组最新试验和来自Cochrane系统性综述数据库中发表的17篇更新的系统性综述的141项临床试验注册中进行了测试。矩阵因子分解方法优于文档相似性方法，中值排名为59，recall@100为60.9%，而文档相似性基线的中值排名为138，recall@100为42.8%。在第二组系统性综述及其更新中，性能最高的方法使用了文档相似性，并给出了67的中位数排名（recall@100为62.9%）。所提出的方法可用于对试验注册进行排序，以减少为系统性综述更新查找相关试验的人工工作量。结果表明，该方法可用作半自动管道的一部分，用于监测潜在的新证据，以纳入审查更新。
英文摘要：
Clinical trial registries can be used to monitor the production of trial evidence and signal when systematic reviews become out of date. However, this use has been limited to date due to the extensive manual review required to search for and screen relevant trial registrations. Our aim was to evaluate a new method that could partially automate the identification of trial registrations that may be relevant for systematic review updates. We identified 179 systematic reviews of drug interventions for type 2 diabetes, which included 537 clinical trials that had registrations in ClinicalTrials.gov. We tested a matrix factorisation approach that uses a shared latent space to learn how to rank relevant trial registrations for each systematic review, comparing the performance to document similarity to rank relevant trial registrations. The two approaches were tested on a holdout set of the newest trials from the set of type 2 diabetes systematic reviews and an unseen set of 141 clinical trial registrations from 17 updated systematic reviews published in the Cochrane Database of Systematic Reviews. The matrix factorisation approach outperformed the document similarity approach with a median rank of 59 and recall@100 of 60.9%, compared to a median rank of 138 and recall@100 of 42.8% in the document similarity baseline. In the second set of systematic reviews and their updates, the highest performing approach used document similarity and gave a median rank of 67 (recall@100 of 62.9%). The proposed method was useful for ranking trial registrations to reduce the manual workload associated with finding relevant trials for systematic review updates. The results suggest that the approach could be used as part of a semi-automated pipeline for monitoring potentially new evidence for inclusion in a review update.
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