在观察性健康研究中开发稳健的可计算表型定义工作流以描述健康与疾病
Developing a Robust Computable Phenotype Definition Workflow to Describe Health and Disease in Observational Health Research
AI总结：
本文针对健康信息学中缺乏严格定义患者人群最佳实践的问题，提出了一个可计算表型定义开发工作流，并通过多个示例展示其在健康信息学中的应用，以支持临床指南和决策支持系统。
摘要：
健康信息学可以为从业者、患者、政策制定者和研究人员在健康与疾病方面所需做出的决策提供依据。健康信息学建立在患者健康数据之上，因此需要对患者健康信息进行编码。这种标准化是计算人群统计指标（如患病率、发病率等）所必需的，这些指标是流行病学等领域常用的度量。关于健康与疾病的可靠决策取决于我们组织、分析和评估包含患者健康数据的数据存储库的能力。虽然已有标准用于在健康信息交换、临床数据存储库和健康数据市场等患者数据源之间构建和分析患者数据，但在健康信息学背景下严格定义患者人群的类似最佳实践尚不存在。将开发疾病定义的最佳实践加以规范化，可以支持临床指南的有效制定，为临床决策支持系统中使用的算法提供信息，并补充患者指南。在本文中，我们提出了一个用于开发表型定义的工作流。该工作流为定义健康与疾病提供了一系列建议。本文中给出了多个示例，以展示该工作流在健康信息学背景下的应用。
英文摘要：
Health informatics can inform decisions that practitioners, patients, policymakers, and researchers need to make about health and disease. Health informatics is built upon patient health data leading to the need to codify patient health information. Such standardization is required to compute population statistics (such as prevalence, incidence, etc.) that are common metrics used in fields such as epidemiology. Reliable decision-making about health and disease rests on our ability to organize, analyze, and assess data repositories that contain patient health data. While standards exist to structure and analyze patient data across patient data sources such as health information exchanges, clinical data repositories, and health data marketplaces, analogous best practices for rigorously defining patient populations in health informatics contexts do not exist. Codifying best practices for developing disease definitions could support the effective development of clinical guidelines, inform algorithms used in clinical decision support systems, and additional patient guidelines. In this paper, we present a workflow for the development of phenotype definitions. This workflow presents a series of recommendations for defining health and disease. Various examples within this paper are presented to demonstrate this workflow in health informatics contexts.
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。
