IEEE/ACM Trans Comput Biol Bioinform. 2021 Mar-Apr;18(2):745-758. doi: 10.1109/TCBB.2019.2937862. Epub 2021 Apr 6.
Disease prediction has the potential to benefit stakeholders such as the government and health insurance companies. It can identify patients at risk of disease or health conditions. Clinicians can then take appropriate measures to avoid or minimize the risk and in turn, improve quality of care and avoid potential hospital admissions. Due to the recent advancement of tools and techniques for data analytics, disease risk prediction can leverage large amounts of semantic information, such as demographics, clinical diagnosis and measurements, health behaviours, laboratory results, prescriptions and care utilisation. In this regard, electronic health data can be a potential choice for developing disease prediction models. A significant number of such disease prediction models have been proposed in the literature over time utilizing large-scale electronic health databases, different methods, and healthcare variables. The goal of this comprehensive literature review was to discuss different risk prediction models that have been proposed based on electronic health data. Search terms were designed to find relevant research articles that utilized electronic health data to predict disease risks. Online scholarly databases were searched to retrieve results, which were then reviewed and compared in terms of the method used, disease type, and prediction accuracy. This paper provides a comprehensive review of the use of electronic health data for risk prediction models. A comparison of the results from different techniques for three frequently modelled diseases using electronic health data was also discussed in this study. In addition, the advantages and disadvantages of different risk prediction models, as well as their performance, were presented. Electronic health data have been widely used for disease prediction. A few modelling approaches show very high accuracy in predicting different diseases using such data. These modelling approaches have been used to inform the clinical decision process to achieve better outcomes.
疾病预测有可能使政府和健康保险公司等利益相关者受益。它可以识别出患有疾病或健康状况风险的患者。然后，临床医生可以采取适当的措施来避免或最小化风险，从而提高护理质量并避免潜在的住院治疗。由于最近在数据分析工具和技术方面取得了进步，疾病风险预测可以利用大量语义信息，例如人口统计学、临床诊断和测量、健康行为、实验室结果、处方和护理利用情况。在这方面，电子健康数据可以成为开发疾病预测模型的潜在选择。随着时间的推移，已经在文献中提出了大量利用大规模电子健康数据库、不同方法和医疗保健变量的疾病预测模型。本次全面文献综述的目的是讨论基于电子健康数据提出的不同风险预测模型。设计了搜索词来查找利用电子健康数据预测疾病风险的相关研究文章。在线学术数据库中搜索检索结果，然后根据所使用的方法、疾病类型和预测准确性对其进行审查和比较。本文全面回顾了利用电子健康数据进行风险预测模型的情况。本研究还讨论了使用电子健康数据对三种常见疾病模型的不同技术的结果进行比较。此外，还介绍了不同风险预测模型的优缺点及其性能。电子健康数据已被广泛用于疾病预测。一些建模方法在使用此类数据预测不同疾病方面显示出非常高的准确性。这些建模方法已被用于为临床决策过程提供信息，以实现更好的结果。
