In this paper, we present a new approach to improving the relevance and reliability of medical IR, which builds upon the concept of Level of Evidence (LoE). LoE framework categorizes medical publications into 7 distinct levels based on the underlying empirical evidence. Despite LoE framework's relevance in medical research and evidence-based practice, only few medical publications explicitly state their LoE. Therefore, we develop a classification model for automatically assigning LoE to medical publications, which successfully classifies over 26 million documents in MEDLINE database into LoE classes. The subsequent retrieval experiments on TREC PM datasets show substantial improvements in retrieval relevance, when LoE is used as a search filter.

翻译：本文提出了一种改进医学信息检索相关性与可靠性的新方法，该方法建立在证据级别（Level of Evidence, LoE）概念之上。LoE框架根据实证证据基础将医学文献划分为7个不同等级。尽管LoE框架在医学研究与循证实践中具有重要意义，但仅有少数医学文献明确标注其LoE等级。为此，我们开发了一种自动为医学文献分配LoE等级的分类模型，成功将MEDLINE数据库中超过2600万篇文献划分至相应的LoE类别。随后在TREC PM数据集上进行的检索实验表明，将LoE作为检索过滤器使用时，检索相关性得到显著提升。