临床信息检索:文献综述
Clinical Information Retrieval: A Literature Review.
作者信息
Sivarajkumar Sonish, Mohammad Haneef Ahamed, Oniani David, Roberts Kirk, Hersh William, Liu Hongfang, He Daqing, Visweswaran Shyam, Wang Yanshan
机构信息
Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA USA.
Department of Information Science, University of Pittsburgh, Pittsburgh, PA USA.
出版信息
J Healthc Inform Res. 2024 Jan 23;8(2):313-352. doi: 10.1007/s41666-024-00159-4. eCollection 2024 Jun.
UNLABELLED
Clinical information retrieval (IR) plays a vital role in modern healthcare by facilitating efficient access and analysis of medical literature for clinicians and researchers. This scoping review aims to offer a comprehensive overview of the current state of clinical IR research and identify gaps and potential opportunities for future studies in this field. The main objective was to assess and analyze the existing literature on clinical IR, focusing on the methods, techniques, and tools employed for effective retrieval and analysis of medical information. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted an extensive search across databases such as Ovid Embase, Ovid Medline, Scopus, ACM Digital Library, IEEE Xplore, and Web of Science, covering publications from January 1, 2010, to January 4, 2023. The rigorous screening process led to the inclusion of 184 papers in our review. Our findings provide a detailed analysis of the clinical IR research landscape, covering aspects like publication trends, data sources, methodologies, evaluation metrics, and applications. The review identifies key research gaps in clinical IR methods such as indexing, ranking, and query expansion, offering insights and opportunities for future studies in clinical IR, thus serving as a guiding framework for upcoming research efforts in this rapidly evolving field. The study also underscores an imperative for innovative research on advanced clinical IR systems capable of fast semantic vector search and adoption of neural IR techniques for effective retrieval of information from unstructured electronic health records (EHRs).
SUPPLEMENTARY INFORMATION
The online version contains supplementary material available at 10.1007/s41666-024-00159-4.
未标注
临床信息检索(IR)通过促进临床医生和研究人员高效获取和分析医学文献,在现代医疗保健中发挥着至关重要的作用。本范围综述旨在全面概述临床IR研究的现状,并确定该领域未来研究的差距和潜在机会。主要目标是评估和分析现有的临床IR文献,重点关注用于有效检索和分析医学信息的方法、技术和工具。我们遵循系统评价和Meta分析的首选报告项目(PRISMA)指南,在Ovid Embase、Ovid Medline、Scopus、ACM数字图书馆、IEEE Xplore和Web of Science等数据库中进行了广泛搜索,涵盖2010年1月1日至2023年1月4日的出版物。严格的筛选过程导致我们的综述纳入了184篇论文。我们的研究结果对临床IR研究领域进行了详细分析,涵盖了出版趋势、数据源、方法、评估指标和应用等方面。该综述确定了临床IR方法中的关键研究差距,如索引、排序和查询扩展,为临床IR的未来研究提供了见解和机会,从而为这一快速发展领域即将开展的研究工作提供了指导框架。该研究还强调了对先进临床IR系统进行创新研究的必要性,这些系统能够进行快速语义向量搜索,并采用神经IR技术从非结构化电子健康记录(EHR)中有效检索信息。
补充信息
在线版本包含可在10.1007/s41666-024-00159-4获取的补充材料。