Uses and opportunities for machine learning in hypertension research

Abstract

Background: 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.

Methods and results: 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.

Conclusion: These examples describe the use of artificial intelligence methods in the field of hypertension.

Keywords: AMI, Acute Myocardial Infarction; CART, Classification and Regression Trees; CNN, Convolution Neural Net; CS/E, Computer Sciences/Engineering; DBP, Diastolic Blood Pressure; Deep neural networks; Disease management; EHR, Electronic Health Record; HF, Heart Failure; Hypertension; ICD, International Classification of Diseases; MIDAS, Myocardial Infarction Data Acquisition System; Machine learning; NPV, Negative Predictive Value; PDN, Personalized Disease Network; PPG, photoplethysmography; PPV, Positive Predictive Value; Personalized disease network; SBP, Systolic Blood Pressure; SVM, Support Vector Machine.

Fig. 1

Machine learning and statistics in the context of data science. Machine learning and statistics are at the core of data science. Other disciplines including engineering/computer sciences, data science, statistics/biostatistics, big data and bioinformatics intersect with machine learning in clinical medicine.

Fig. 2

Conceptual schema of the workflow for convolution neural networks (CNN). CNNs process the data by layers of convolution filters that are followed by a second set of layers of a fully CNN also known as a multilayer perceptron.