Emergence of digital biomarkers to predict and modify treatment efficacy: machine learning study.

ABSTRACT:

Objectives

Development of digital biomarkers to predict treatment response to a digital behavioural intervention.

Design

Machine learning using random forest classifiers on data generated through the use of a digital therapeutic which delivers behavioural therapy to treat cardiometabolic disease. Data from 13 explanatory variables (biometric and engagement in nature) generated in the first 28 days of a 12-week intervention were used to train models. Two levels of response to treatment were predicted: (1) systolic change ≥10 mm Hg (SC model), and (2) shift down to a blood pressure category of elevated or better (ER model). Models were validated using leave-one-out cross validation and evaluated using area under the curve receiver operating characteristics (AUROC) and specific

SUBMITTER: Guthrie NL 

PROVIDER: S-EPMC6661657 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature