Code to supplement the analysis presented in 'Discovery of Parkinson's disease states using a machine learning approach'. If you are would like to use the exact model or cohort presented in the paper, please reach out to us directly.
The code in this repositiory leverages data made available via PPMI. For information about the dataset and to gain access, visit ppmi-info.org.
The analysis is organized as follows
Several notebooks are used to pre-process the raw data from ppmi. Motor_Data_Processing.ipynb, Non_Motor_Data_Processing.ipynb, Demographics_Data_Processing.ipynb process those measures respectively. Full_Data_Processing.ipynb merges those sources and addresses date entry errors. LEDD_Data_Processing.ipynb processes the medication information using functionality provided in calc_ledd.py. Train_Test_Split.ipynb creates the train/test split used in analysis.
The processed data is used as input to a contrastive latent variable model as demonstrated in Learn_and_Apply_cLVM.ipynb. The code for the model can be found here.
Personalized input-output hidden Markov model
The latent representation is used as input to the personalized input-output hidden Markov model in Learn_and_Apply_PIOHMM.ipynb. The code for the model can be found here The latent representation data from the cLVM must first be processed into a format the progression model can use as demonstrated in LD_Data_Processing.ipynb
Manuscript_Figures.ipynb presents code the generates the primary figures of the manuscript.