Initial code release for research papers Predicting Alzheimer’s disease progression trajectory and clinical subtypes using machine learning and Learning the progression and clinical subtypes of Alzheimer's disease from longitudinal clinical data, ML4H@NeurIPS2018. This work studies the recent developments in machine learning techniques to predict disease progression and trajectory of Alzheimer’s disease and further classify the disease into different etiological subtypes.
If you use this code, please consider citing our work:
@article{satone2018learning,
title={Learning the progression and clinical subtypes of Alzheimer's disease from longitudinal clinical data},
author={Satone, Vipul and Kaur, Rachneet and Faghri, Faraz and Nalls, Mike A and Singleton, Andrew B and Campbell, Roy H},
journal={arXiv preprint arXiv:1812.00546},
year={2018}
}
@article{satone2019predicting,
title={Predicting Alzheimer’s disease progression trajectory and clinical subtypes using machine learning},
author={Satone, Vipul K and Kaur, Rachneet and Leonard, Hampton and Iwaki, Hirotaka and Sargent, Lana and Scholz, Sonja W and Nalls, Mike A and Singleton, Andrew B and Faghri, Faraz and Campbell, Roy H and others},
journal={bioRxiv},
pages={792432},
year={2019},
publisher={Cold Spring Harbor Laboratory}
}