是什么造就了一个性能卓越的精准医学搜索引擎？以系统的方式追踪系统的主要特征
What Makes a Top-Performing Precision Medicine Search Engine? Tracing Main System Features in a Systematic Way
摘要：
从2017年到2019年，文本检索会议（TREC）使用医学出版物（PubMed）和临床试验的文件举办了一项关于精准医学的挑战任务。尽管在这些评估活动中进行了大量的性能测量，但科学界仍然不确定单个系统功能及其权重对整体系统性能的影响。为了克服这一解释的差距，我们首先确定了最佳的功能配置使用顺序模型为基础的算法配置（SMAC）程序，并将其输出到基于BM 25的搜索引擎。然后，我们进行了一项消融研究，系统地评估相关系统功能的个人贡献：BM 25参数，查询类型和加权模式，查询扩展，停止词过滤和关键字提升。为了进行评估，我们采用了来自三个TREC-PM分期付款的黄金标准数据，使用共同共享的infNDCG指标来评估不同功能的有效性。
英文摘要：
From 2017 to 2019 the Text REtrieval Conference (TREC) held a challenge task on precision medicine using documents from medical publications (PubMed) and clinical trials. Despite lots of performance measurements carried out in these evaluation campaigns, the scientific community is still pretty unsure about the impact individual system features and their weights have on the overall system performance. In order to overcome this explanatory gap, we first determined optimal feature configurations using the Sequential Model-based Algorithm Configuration (SMAC) program and applied its output to a BM25-based search engine. We then ran an ablation study to systematically assess the individual contributions of relevant system features: BM25 parameters, query type and weighting schema, query expansion, stop word filtering, and keyword boosting. For evaluation, we employed the gold standard data from the three TREC-PM installments to evaluate the effectiveness of different features using the commonly shared infNDCG metric.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
