Multi-Source_Ensemble_Learning_Neural_Networks

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Multi-Source_Ensemble_Learning_Neural_Networks

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The code used for the IEEE Transactions on Biomedical Engineering paper entitled 'Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-wise Missing Data'

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# Multi-Source_Ensemble_Learning_Neural_Networks
The code used for the IEEE Transactions on Biomedical Engineering paper entitled 'Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-wise Missing Data'

This is the code used for training, validating, and testing the Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) described in Section II-C of the manuscript.

Additionally, the customisable script used to download the raw mPower data from Synapse is included and comes with a full description as how to use.

Please contact john.prince@eng.ox.ac.uk for any questions regarding the code or the manuscript.

If using any of this code, including the Synapse data download client, please cite the following manuscripts:

Prince, J., Andreotti, F. and De Vos, M., 2018. Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-wise Missing Data. IEEE Transactions on Biomedical Engineering.

Prince, J., Arora, S. and de Vos, M., 2018. Big data in Parkinson’s disease: using smartphones to remotely detect longitudinal disease phenotypes. Physiological measurement, 39(4), p.044005.

Prince, J. and de Vos, M., 2018, July. A Deep Learning Framework for the Remote Detection of Parkinson’s Disease Using Smart-Phone Sensor Data. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 3144-3147). IEEE.

Code Includes:
1) main.py

2) modelDefintions.py

3) usefulMethods.py

Multi-Source_Ensemble_Learning_Neural_Networks

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