Using self-supervised feature learning to improve the use of pulse oximeter signals to predict paediatric hospitalization.

ABSTRACT: Background: The success of many machine learning applications depends on knowledge about the relationship between the input data and the task of interest (output), hindering the application of machine learning to novel tasks. End-to-end deep learning, which does not require intermediate feature engineering, has been recommended to overcome this challenge but end-to-end deep learning models require large labelled training data sets often unavailable in many medical applications. In this study, we trained self-supervised learning (SSL) models for automatic feature extraction from raw photoplethysmography (PPG) obtained using a pulse oximeter, with the aim of predicting paediatric hospitalization.  Methods: We compared logistic regression models fitted using features extracted u

SUBMITTER: Mwaniki P 

PROVIDER: S-EPMC10280037 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature