In recent decades, the implementation of electronic health record (EHR) systems has been evolving worldwide, leading to the creation of immense data volume in healthcare. Moreover, there has been a call for research studies to enhance personalized medicine and develop clinical decision support systems (CDSS) by analyzing the available EHR data. In EHR data, usually, there are millions of patients records with hundreds of features collected over a long period of time. This enormity of EHR data poses significant challenges, one of which is dealing with many variables with very high degrees of missing values. In this study, the data quality issue of incompleteness in EHR data is discussed, and a framework called ‘Missing Care’ is introduced to address this issue. Using Missing Care, researchers will be able to select the most important variables at an acceptable missing values degree to develop predictive models with high predictive power. Moreover, Missing Care is applied to analyze a unique, large EHR data to develop a CDSS for detecting Parkinson's disease. Parkinson is a complex disease, and even a specialist's diagnosis is not without error. Besides, there is a lack of access to specialists in more remote areas, and as a result, about half of the patients with Parkinson's disease in the US remain undiagnosed. The developed CDSS can be integrated into EHR systems or utilized as an independent tool by healthcare practitioners who are not necessarily specialists; therefore, making up for the limited access to specialized care in remote areas.

在最近的几十年中，电子健康记录（EHR）系统的实施在全球范围内不断发展，导致在医疗保健领域创建了巨大的数据量。此外，已经呼吁进行研究研究，以通过分析可用的EHR数据来增强个性化医学并开发临床决策支持系统（CDSS）。通常，在EHR数据中，有数百万的患者记录是长期收集的数百个特征。EHR数据的庞大性带来了重大挑战，其中之一就是要处理许多缺失值非常高的变量。在这项研究中，讨论了EHR数据中数据不完整的数据质量问题，并引入了一个名为“缺少护理”的框架来解决此问题。使用缺少护理，研究人员将能够在可接受的缺失值程度下选择最重要的变量，从而开发出具有较高预测能力的预测模型。此外，Missing Care可用于分析独特的大型EHR数据，从而开发出CDSS来检测帕金森氏病。帕金森病是一种复杂的疾病，即使是专家的诊断也并非没有错误。此外，在更偏远的地区也缺乏与专家接触的机会，因此，美国约有一半的帕金森氏病患者仍未得到诊断。可以将开发的CDSS集成到EHR系统中，或者由不一定是专家的医疗从业人员用作独立工具；因此，弥补了偏远地区获得专业护理的机会有限。