PMC-Patients：用于基准测试基于检索的临床决策支持系统的大规模患者摘要与关系数据集
PMC-Patients: A Large-scale Dataset of Patient Summaries and Relations for Benchmarking Retrieval-based Clinical Decision Support Systems
AI总结：
本文提出PMC-Patients，一个包含167k患者摘要及大规模关系标注的数据集，用于基准测试基于检索的临床决策支持系统，涵盖患者到文章和患者到患者检索任务，并评估多种检索方法。
摘要：
目的：基于检索的临床决策支持（ReCDS）可以通过为给定患者提供相关文献和相似患者来辅助临床工作流程。然而，ReCDS系统的发展一直受到缺乏多样化患者集合和公开可用的大规模患者级标注数据集的严重阻碍。在本文中，我们旨在使用一个名为PMC-Patients的新数据集来定义和基准测试两个ReCDS任务：患者到文章检索（ReCDS-PAR）和患者到患者检索（ReCDS-PPR）。方法：我们使用简单的启发式方法从PubMed Central文章中提取患者摘要，并利用PubMed引文图来定义患者-文章相关性和患者-患者相似性。我们还在PMC-Patients基准上实现并评估了几种ReCDS系统，包括稀疏检索器、密集检索器和最近邻检索器。我们进行了多项案例研究以展示PMC-Patients的临床实用性。结果：PMC-Patients包含167k条患者摘要，带有3.1M条患者-文章相关性标注和293k条患者-患者相似性标注，这是ReCDS最大规模的资源，也是最大的患者集合之一。人工评估和分析表明，PMC-Patients是一个具有高质量标注的多样化数据集。对各种ReCDS系统的评估表明，PMC-Patients基准具有挑战性，需要进一步研究。结论：我们提出了PMC-Patients，这是一个大规模、多样化且公开可用的患者摘要数据集，具有最大规模的患者级关系标注。基于PMC-Patients，我们正式定义了ReCDS系统的两个基准任务，并评估了各种现有的检索方法。PMC-Patients可以极大地促进ReCDS系统的方法论研究，并展示出真实的临床实用性。
英文摘要：
Objective: Retrieval-based Clinical Decision Support (ReCDS) can aid clinical workflow by providing relevant literature and similar patients for a given patient. However, the development of ReCDS systems has been severely obstructed by the lack of diverse patient collections and publicly available large-scale patient-level annotation datasets. In this paper, we aim to define and benchmark two ReCDS tasks: Patient-to-Article Retrieval (ReCDS-PAR) and Patient-to-Patient Retrieval (ReCDS-PPR) using a novel dataset called PMC-Patients. Methods: We extract patient summaries from PubMed Central articles using simple heuristics and utilize the PubMed citation graph to define patient-article relevance and patient-patient similarity. We also implement and evaluate several ReCDS systems on the PMC-Patients benchmarks, including sparse retrievers, dense retrievers, and nearest neighbor retrievers. We conduct several case studies to show the clinical utility of PMC-Patients. Results: PMC-Patients contains 167k patient summaries with 3.1M patient-article relevance annotations and 293k patient-patient similarity annotations, which is the largest-scale resource for ReCDS and also one of the largest patient collections. Human evaluation and analysis show that PMC-Patients is a diverse dataset with high-quality annotations. The evaluation of various ReCDS systems shows that the PMC-Patients benchmark is challenging and calls for further research. Conclusion: We present PMC-Patients, a large-scale, diverse, and publicly available patient summary dataset with the largest-scale patient-level relation annotations. Based on PMC-Patients, we formally define two benchmark tasks for ReCDS systems and evaluate various existing retrieval methods. PMC-Patients can largely facilitate methodology research on ReCDS systems and shows real-world clinical utility.
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。
