With the emergence of electronic health records, the reuse of clinical data offers new perspectives for the diagnosis and management of patients with rare diseases. However, there are many obstacles to the repurposing of clinical data. The development of decision support systems depends on the ability to recruit patients, extract and integrate the patients' data, mine and stratify these data, and integrate the decision support algorithm into patient care. This last step requires an adaptability of the electronic health records to integrate learning health system tools. In this literature review, we examine the research that provides solutions to unlock these barriers and accelerate translational research: structured electronic health records and free-text search engines to find patients, data warehouses and natural language processing to extract phenotypes, machine learning algorithms to classify patients, and similarity metrics to diagnose patients. Medical informatics is experiencing an impellent request to develop decision support systems, and this requires ethical considerations for clinicians and patients to ensure appropriate use of health data.

中文翻译：

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电子病历用于诊断罕见疾病。

随着电子健康记录的出现，临床数据的重用为罕见病患者的诊断和管理提供了新的视角。但是，重新利用临床数据存在许多障碍。决策支持系统的开发取决于招募患者，提取和整合患者数据，对这些数据进行挖掘和分层以及将决策支持算法集成到患者护理中的能力。最后一步要求电子健康记录具有适应性，以集成学习健康系统工具。在这篇文献综述中，我们研究了提供解决方案以解锁这些障碍并加速翻译研究的研究：结构化的电子病历和自由文本搜索引擎，用于寻找患者，数据仓库和自然语言处理来提取表型，使用机器学习算法对患者进行分类，并使用相似性指标来诊断患者。医学信息学正迫切需要开发决策支持系统，这要求临床医生和患者在伦理上进行考虑，以确保适当使用健康数据。