Analysis of continuous monitoring device data

This paper introduces a methodology for processing continuous monitoring device data, such as data from a wearable digital device or continuous telemetered data, to estimate outcomes like systolic blood pressure or treatment effects. One of the challenges of analyzing this type of data is to find a suitable binning or scaling to compress the information for improving outcome predictions. Another challenge is to select and weight the features to be included in the computational model. The new methodology consists of a combination of feature selection and feature weighting incorporated into the LASSO and the elastic net methods, which addresses both issues simultaneously. The compression of continuous data into weighted discretized data is a prominent issue in the development of AI methodology that is applied to wearable DHT devices. The new methodology was applied to a Fitbit data set from a Hong Kong elderly center study.