Using a machine learning approach to predict the outcome of intervention programs in children with unilateral cerebral palsy

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In order to improve upper limb function in unilateral cerebral palsy (uCP), two different approaches have been developed over the past years. One approach is modified Constraint-Induced Movement Therapy (mCIMT), which involves constraining the less-affected upper limb and intensively training the affected arm and hand. The other approach is Bimanual Training (BiT), which focuses on the use of both hands. While both therapies have proven to be effective on a group level, there are large inter-individual differences. To detect these differences beforehand, machine learning models were trained to predict the outcome of intervention in 45 children by using predictors obtained prior to intervention. These include several demographic features as well as hand-capacity and manual ability scores and EEG features. In the end, intervention outcomes were predicted on ABILHand-Kids scores, CHEQ scores, reaction times and Box and Blocks scores. The explained variance on the predictions exceeded 0.5 for ABILHand-Kids and CHEQ scores, 0.6 for the reaction times and 0.7 on the Box and Blocks task. Prediction errors were below standard deviation for the CHEQ and Box and Blocks scores. Addition of EEG features did not change the prediction error. This approach shows promising results in predicting the outcome of intervention in individual children. It can be used in future to create optimal tailored interventions for individual children with uCP.
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