Telfer School of Management, University of Ottawa, Ottawa, Canada; Interdepartmental Centre for Health Technologies, University of Pavia, Italy.
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA; Interdepartmental Centre for Health Technologies, University of Pavia, Italy.
J Biomed Inform. 2018 Jul;83:87-96. doi: 10.1016/j.jbi.2018.06.001. Epub 2018 Jun 1.
Evidence-based medicine is the most prevalent paradigm adopted by physicians. Clinical practice guidelines typically define a set of recommendations together with eligibility criteria that restrict their applicability to a specific group of patients. The ever-growing size and availability of health-related data is currently challenging the broad definitions of guideline-defined patient groups. Precision medicine leverages on genetic, phenotypic, or psychosocial characteristics to provide precise identification of patient subsets for treatment targeting. Defining a patient similarity measure is thus an essential step to allow stratification of patients into clinically-meaningful subgroups. The present review investigates the use of patient similarity as a tool to enable precision medicine. 279 articles were analyzed along four dimensions: data types considered, clinical domains of application, data analysis methods, and translational stage of findings. Cancer-related research employing molecular profiling and standard data analysis techniques such as clustering constitute the majority of the retrieved studies. Chronic and psychiatric diseases follow as the second most represented clinical domains. Interestingly, almost one quarter of the studies analyzed presented a novel methodology, with the most advanced employing data integration strategies and being portable to different clinical domains. Integration of such techniques into decision support systems constitutes and interesting trend for future research.
循证医学是医生采用的最流行的范例。临床实践指南通常定义了一组建议以及资格标准，这些建议和标准将其适用性限制在特定的患者群体。目前，与健康相关的数据不断增长和可用性，正在挑战指南定义的患者群体的广泛定义。精准医学利用遗传、表型或社会心理特征，为治疗目标提供患者亚组的精确识别。因此，定义患者相似性度量是将患者分层为具有临床意义的亚组的必要步骤。本综述调查了患者相似性作为一种工具在精准医学中的应用。从四个方面分析了 279 篇文章：考虑的数据类型、应用的临床领域、数据分析方法和研究结果的转化阶段。采用分子谱分析和聚类等标准数据分析技术的癌症相关研究构成了检索研究的大部分。慢性和精神疾病是第二大代表性临床领域。有趣的是，近四分之一的分析研究提出了一种新方法，其中最先进的方法采用数据集成策略，并可应用于不同的临床领域。将这些技术集成到决策支持系统中是未来研究的一个有趣趋势。
