https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-024-02736-4
Objective data from newborn resuscitation activities can be used to generate temporal timelines of the executed resuscitation. This paper introduces methods for analyzing newborn resuscitation activity timelines, through visualization, aggregation, redundancy and dimensionality reduction. Video and sensor datasets from Stavanger University Hospital (n=108 resuscitation episodes) and Haydom Lutheran Hospital (n=76 episodes) were used. We propose an encoding generator with unique codes for combination of activities. A visualization of aggregated episodes is proposed using sparse nearest neighbor graph, shown to be useful to compare datasets and give insights. Finally, we propose a method consisting of an autoencoder trained for reducing redundancy in encoded resuscitation timeline descriptions, followed by a neighborhood component analysis for dimensionality reduction. Visualization of the resulting features shows very good class separability and potential for clustering the resuscitation files according to the outcome of the newborns as dead, admitted to neonatal unit or normal.