Detecting at-risk mental states for psychosis (ARMS) using machine learning ensembles and facial features.

ABSTRACT:

Aims

Our study aimed to develop a machine learning ensemble to distinguish "at-risk mental states for psychosis" (ARMS) subjects from control individuals from the general population based on facial data extracted from video-recordings.

Methods

58 non-help-seeking medication-naïve ARMS and 70 healthy subjects were screened from a general population sample. At-risk status was assessed with the Structured Interview for Prodromal Syndromes (SIPS), and "Subject's Overview" section was filmed (5-10 min). Several features were extracted, e.g., eye and mouth aspect ratio, Euler angles, coordinates from 51 facial landmarks. This elicited 649 facial features, which were further selected using Gradient Boosting Machines (AdaBoost combined with Random Forests). Data was split in 70/30 f

SUBMITTER: Loch AA 

PROVIDER: S-EPMC10448183 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature