Prediction of short-acting beta-agonist usage in patients with asthma using temporal-convolutional neural networks.

ABSTRACT:

Objective

Changes in short-acting beta-agonist (SABA) use are an important signal of asthma control and risk of asthma exacerbations. Inhaler sensors passively capture SABA use and may provide longitudinal data to identify at-riskpatients. We evaluate the performance of several ML models in predicting daily SABA use for participants with asthma and determine relevant features for predictive accuracy.

Methods

Participants with self-reported asthma enrolled in a digital health platform (Propeller Health, WI), which included a smartphone application and inhaler sensors that collected the date and time of SABA use. Linear regression, random forests, and temporal convolutional networks (TCN) were applied to predict expected SABA puffs/person/day from SABA usage and environmental

SUBMITTER: Hirons N 

PROVIDER: S-EPMC10602590 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature