kseverso/discovery-of-pd-states-using-ml

discovery-of-pd-states-using-ml's Issues

Academic Communication

Dear Dr Kristen A Severson:
I am a current PhD students of Zhejiang University in China. I major in "Biomedical Engineering". Recently, I found one of your articles, titled "Discovery of Parkinson’s disease states and disease progression modelling: a longitudinal data study using machine learning" in The Lancet Digital Health. This is a very interesting and valuable project, I found it may help me achieve my goals in this research field. This would make a really positive contribution to my work. When I wants to use the methods of article to apply to my research, but I can't implement using the cLVM and PIOHMM model to achieve low-dimensional representation and discover latent state of disease, I am wondering if you could kindly send me the source code and the necessary information about it. I promise they will be used only for research purposed. Due to your mailbox is set to automatically return,you cannot receive my emails. So,I have chosen to leave you a message on github.
Thank you very much for your kind consideration and I am looking forward to your early reply.
Sincerely: Zhang Suixia
My Email address is: [email protected]

Data Set Changes

Dear Dr Kristen A Severson:
I am a current PhD students of Zhejiang Sci-Tech University in China. I major in "Biomedical Engineering". Recently, I found one of your articles, titled "Discovery of Parkinson’s disease states and disease progression modelling: a longitudinal data study using machine learning" in The Lancet Digital Health.When I run Demographics_ Data_ In Processing.ipynb, I did not find Randomization on the PPMI official website_ Table.csv dataset. I wonder if there are other datasets that can replace it.
Thank you very much for your kind consideration and I am looking forward to your early reply.
Sincerely: Xu Zeqi
My Email address is:[email protected]

Could i use the exact model or cohort presented in the paper?

Hi, kseverso
i'm a Computer Postgraduate ，i have read the paper and want to reproduce it, Could i got the exact model or cohort presented in the paper? If possible, how can I contact you?

Problems related to model loss function

I'm really sorry to disturb you again. Actually, I have already raised an issue in 'DiseaseProgressionModeling-HMM
', but I didn't get a reply. I wonder if you omitted it. Here is the original question:

Dear kseverso:
Hello, I am trying to reproduce the PD progress model, but have encountered some difficulties. I hope to get some help from you.

I followed the steps you preprocessed in "Discovery-of-PD-States-using-ML", processed the PPMI dataset (I made some changes due to the dataset changes), and then applied it to the PIOHMM. However, even if I set k=8 and hope to get 8 states, I only get 3-4 results in the training set, which are often in the time series t<5(T=31) I have already get the final state, for example, states 3 and 4. This situation becomes more and more significant with more iterations, and may even end up with only two states.
Besides, I may not know exactly what the model parameter learning steps mean. Are the ELBO and log_prob (self.ll) obtained at each iteration concepts similar to 'loss' in neural networks? According to my observation, ELBO was around 30,000 after the first iteration, then changed to around -10000 after the second iteration, and remained negative at -100000~-120000. Log_prob, on the other hand, keeps at about 110000, iterating at the learning rate of 1E-18, and fluctuates around 120000 or so when it reaches 20 times (convergence is impossible even using usE_CC convergence standard). I wonder how ELBO and log_prob change when you apply it into PPMI datasets? And what orders of magnitude are they?
Looking forward to your reply!