Ensemble and Personalized Transformer Models for Subject Identification and Relapse Detection in E-Prevention Challenge
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2023)
摘要
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
nd
on the subject identification task (with an accuracy of 93.85%) and 1
st
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.
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关键词
CNN model,devised solutions,e-Prevention Challenge,ensemble scheme,final submitted solutions,ICASSP 2023 conference,relapse detection task,subject identification task,transformer-based ones
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