Applying machine learning in motor activity time series of depressed bipolar and unipolar patients.
Petter Jakobsen, Enrique Garcia-Ceja, Michael Alexander Riegler, Lena Antonsen Stabell, Tine Nordgreen, Jim Tørresen, Ole Bernt Fasmer, Ketil Joachim Oedegaard
This study uses machine learning to analyze motor activity data from depressed patients and healthy controls, finding that a deep neural network achieves high accuracy in distinguishing between the groups.
PLoS ONE · Aug 24, 2020Peer-reviewedBioinformatics