Amaratunga Dhammika, Cabrera Javier, Sargsyan Davit, Kostis John B, Zinonos Stavros, Kostis William J
Cardiovascular Institute, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA.
Department of Statistics, Rutgers University, Piscataway, NJ 08854, USA.
Int J Cardiol Hypertens. 2020 Mar 19;5:100027. doi: 10.1016/j.ijchy.2020.100027. eCollection 2020 Jun.
Artificial intelligence (AI) promises to provide useful information to clinicians specializing in hypertension. Already, there are some significant AI applications on large validated data sets.
This review presents the use of AI to predict clinical outcomes in big data i.e. data with high volume, variety, veracity, velocity and value. Four examples are included in this review. In the first example, deep learning and support vector machine (SVM) predicted the occurrence of cardiovascular events with 56%-57% accuracy. In the second example, in a data base of 378,256 patients, a neural network algorithm predicted the occurrence of cardiovascular events during 10 year follow up with sensitivity (68%) and specificity (71%). In the third example, a machine learning algorithm classified 1,504,437 patients on the presence or absence of hypertension with 51% sensitivity, 99% specificity and area under the curve 87%. In example four, wearable biosensors and portable devices were used in assessing a person's risk of developing hypertension using photoplethysmography to separate persons who were at risk of developing hypertension with sensitivity higher than 80% and positive predictive value higher than 90%. The results of the above studies were adjusted for demographics and the traditional risk factors for atherosclerotic disease.
These examples describe the use of artificial intelligence methods in the field of hypertension.
人工智能有望为高血压专科临床医生提供有用信息。目前，在经过充分验证的大型数据集上已经有了一些重要的人工智能应用。
本综述介绍了人工智能在大数据（即具有高容量、多样性、准确性、速度和价值的数据）中预测临床结局的应用。本综述包含四个例子。在第一个例子中，深度学习和支持向量机（SVM）预测心血管事件发生的准确率为56%-57%。在第二个例子中，在一个包含378,256名患者的数据库中，一种神经网络算法在10年随访期间预测心血管事件发生的敏感性为68%，特异性为71%。在第三个例子中，一种机器学习算法对1,504,437名患者是否患有高血压进行分类，敏感性为51%，特异性为99%，曲线下面积为87%。在第四个例子中，可穿戴生物传感器和便携式设备被用于评估一个人患高血压的风险，使用光电容积脉搏波描记法来区分有患高血压风险的人，敏感性高于80%，阳性预测值高于90%。上述研究结果针对人口统计学和动脉粥样硬化疾病的传统风险因素进行了调整。
这些例子描述了人工智能方法在高血压领域的应用。
