Ensemble and Personalized Transformer Models for Subject Identification and Relapse Detection in E-Prevention Challenge

Salvatore Calcagno, Raffaele Mineo, Daniela Giordano, Concetto Spampinato

Abstract: In this short paper, we present the devised solutions for the subject identification and relapse detection tasks, which are part of the e-Prevention Challenge hosted at the ICASSP 2023 conference [1] [2] [3]. We specifically design an ensemble scheme of six models - five transformer-based ones and a CNN model - for the identification of subjects from wearable devices, while a personalized - one for each subject - scheme is used for relapse detection in psychotic disorder. Our final submitted solutions yield top performance on both tracks of the challenge: we ranked 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> on the subject identification task (with an accuracy of 93.85%) and 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> on the relapse detection task (with a ROC-AUC and PR-AUC of about 0.65). Code and details are available at https://github.com/perceivelab/e-prevention-icassp-2023.