Abstract：
Routinely collected electronic health records (EHRs) data contain a vast amount of valuable information for conducting epidemiological studies. With the right tools, we can gain insights into disease processes and development, identify the best treatment and develop accurate models for predicting outcomes. Our recent systematic review has found that the number of big data studies from Hong Kong has rapidly increased since 2015, with an increasingly common application of artificial intelligence (AI). The advantages of big data are that i) the models developed are highly generalisable to the population, ii) multiple outcomes can be determined simultaneously, iii) ease of cross-validation by for model training, development and calibration, iv) huge numbers of useful variables can be analyzed, v) static and dynamic variables can be analyzed, vi) non-linear and latent interactions between variables can be captured, vii) artificial intelligence approaches can enhance the performance of prediction models. In this paper, we will provide several examples (cardiovascular disease, diabetes mellitus, Brugada syndrome, long QT syndrome) to illustrate efforts from a multi-disciplinary team to identify data from different modalities to develop models using territory-wide datasets, with the possibility of real-time risk updates by using new data captured from patients. The benefit is that only routinely collected data are required for developing highly accurate and high-performance models. AI-driven models outperform traditional models in terms of sensitivity, specificity, accuracy, area under the receiver operating characteristic and precision-recall curve, and F1 score. Web and/or mobile versions of the risk models allow clinicians to risk stratify patients quickly in clinical settings, thereby enabling clinical decision-making. Efforts are required to identify the best ways of implementing AI algorithms on the web and mobile apps.
摘要：
常规收集的电子健康记录（EHR）数据包含大量有价值的信息，可用于进行流行病学研究。有了正确的工具，我们可以深入了解疾病的过程和发展，确定最佳治疗方法，并开发准确的模型来预测结果。我们最近的系统评估发现，自2015年以来，香港的大数据研究数量迅速增加，人工智能（AI）的应用越来越普遍。大数据的优势在于，i)开发的模型对人群具有高度的普适性，ii)可以同时确定多个结果，iii)模型训练易于交叉验证，开发和校准，iv)可以分析大量有用的变量，V)可以分析静态和动态变量，vi)可以捕获变量之间的非线性和潜在相互作用，vii)人工智能方法可以提高预测模型的性能。在本文中,我们将提供几个例子(心血管疾病，糖尿病,Brugada综合征,长QT综合征)来说明多学科团队努力识别来自不同模式的数据，以使用全域数据集开发模型，通过使用从患者中捕获的新数据进行实时风险更新的可能性。好处是，只需要常规收集的数据来开发高度精确和高性能的模型。人工智能驱动的模型在灵敏度方面优于传统模型，特异性，准确度,接收器工作特性和精确召回曲线下的面积，F1得分。网络和/或移动版本的风险模型允许临床医生在临床环境中快速对患者进行风险分层，从而使临床决策。需要努力确定在网络和移动应用程序上实施AI算法的最佳方法。
