Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  investigated the feasibility of using feedforward and multilayer perceptrons to predict the performance of chronic users of cocaine on the interference component of the Stroop color-word task after a one-month period of enforced abstinence. A total of 77 individuals who met the diagnostic criteria for cocaine dependence or polydrug dependence volunteered to be admitted to a special research unit where they remained abstinent from drugs of abuse for at least one month. On two separate occasions, once within 3 days of admission and again at least 28 days following admission, participants completed a battery of neuropsychological tests. The output variable to be predicted by the model was performance on the interference component of the Stroop color-word task. Inputs to the model included factor scores derived from a principle components analysis of the performance on the neuropsychological test battery administered within 3 days of admission to the unit as ll as reported levels of cocaine and alcohol consumption during the month prior to the period of enforced abstinence. The network implemented contained two hidden layers and was trained using a standard error backpropagation algorithm. Several versions of the model using this same general architecture re evaluated. Training was very successful, routinely reducing average RMS error to <.05, with median correlation beten predicted and criterion values of over r=.86. Generalization of the training to randomly selected sets of 15 test observations not included in the training sample shod a median r=.64. The networks re moderately successful in predicting outcomes for novel exemplars. These preliminary results suggest that neural network approaches can provide a useful characterization of the dynamics of cognitive recovery during abstinence from drugs of abuse that could lead to the development of more efficacious therapeutic interventions aimed at reducing the use of illicit and/or harmful substances.
['Frank R. Funderburk', 'Karen I. Bolla', 'Jean Lud Cadet']
I am writing a research paper on a idea Multilayer perceptrons can predict cognitive status during recovery from chronic substance misuse: implications for individualized treatment planning
[{"from": "human", "value": "I am writing a research paper on a idea Multilayer perceptrons can predict cognitive status during recovery from chronic substance misuse: implications for individualized treatment planning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  investigated the feasibility of using feedforward and multilayer perceptrons to predict the performance of chronic users of cocaine on the interference component of the Stroop color-word task after a one-month period of enforced abstinence. A total of 77 individuals who met the diagnostic criteria for cocaine dependence or polydrug dependence volunteered to be admitted to a special research unit where they remained abstinent from drugs of abuse for at least one month. On two separate occasions, once within 3 days of admission and again at least 28 days following admission, participants completed a battery of neuropsychological tests. The output variable to be predicted by the model was performance on the interference component of the Stroop color-word task. Inputs to the model included factor scores derived from a principle components analysis of the performance on the neuropsychological test battery administered within 3 days of admission to the unit as ll as reported levels of cocaine and alcohol consumption during the month prior to the period of enforced abstinence. The network implemented contained two hidden layers and was trained using a standard error backpropagation algorithm. Several versions of the model using this same general architecture re evaluated. Training was very successful, routinely reducing average RMS error to <.05, with median correlation beten predicted and criterion values of over r=.86. Generalization of the training to randomly selected sets of 15 test observations not included in the training sample shod a median r=.64. The networks re moderately successful in predicting outcomes for novel exemplars. These preliminary results suggest that neural network approaches can provide a useful characterization of the dynamics of cognitive recovery during abstinence from drugs of abuse that could lead to the development of more efficacious therapeutic interventions aimed at reducing the use of illicit and/or harmful substances."}]