N Hann, A Asghar, W Owen, N Asal
Smoking: high hazards in high school
The Journal of the Oklahoma State Medical Association
study introduces divergent selective focused multiheads selfattention network dsfmanet innovative deep learning model devised automatically predict hamilton depression rating scale17 hamd17 scores patients depression model introduces multibranch structure subbands artificially configures attention focus factors various branches resulting distinct attention distributions different subbands experimental results demonstrate dsfmanet processes subband data performance surpasses current benchmarks key metrics mean square error mse mean absolute error mae root mean square error rmse coefficient determination r2 success particularly evident terms mse mae performance subband data significantly superior highlighting models potential accurately predicting hamd17 scores concurrently experiment also compared models performance subband fullband data affirming superiority selective focus attention mechanism electroencephalography eeg signal processing dsfmanet utilizing subband data exhibits higher data processing efficiency reduced model complexity findings study underscore significance employing deep learning models based subband analysis depression diagnosis dsfmanet model effectively enhances accuracy depression diagnosis also offers valuable research directions similar applications future deep learningbased automated approach effectively ascertain hamd17 scores patients depression thus offering accurate reliable support clinical decisionmaking