Odette Wegwarth, Tammy C Hoffmann, Ben Goldacre, Claudia Spies, Helge A Giese
General practitioners' risk literacy and real-world prescribing of potentially hazardous drugs: a cross-sectional study
BMJ quality & safety
alzheimers disease ad progressive neurodegenerative condition early intervention help slow progression however integrating multidimensional information deep convolutional networks increases model parameters affecting diagnosis accuracy efficiency hindering clinical diagnostic model deployment multimodal neuroimaging offer precise diagnostic results multitask modeling classification regression tasks enhance performance stability ad diagnosis study proposes hierarchical attentionbased multitask multimodal fusion model hammf leverages multimodal neuroimaging data concurrently learn ad classification tasks cognitive score regression age regression tasks using attentionbased techniques firstly preprocess mri pet image data obtain two modal data containing distinct information next incorporate novel contextual hierarchical attention module cham aggregate multimodal features module employs channel spatial attention extract finegrained pathological features unimodal image data across various dimensions using attention mechanisms transformer effectively capture correlated features multimodal inputs lastly adopt multitask learning model investigate influence different variables diagnosis primary classification task secondary regression task optimal multitask prediction performance experiments utilized mri pet images 720 subjects alzheimers disease neuroimaging initiative adni dataset results show proposed model achieves overall accuracy 9315 adnc recognition visualization results demonstrate strong pathological feature recognition performance