Let's say I have a medical dataset/EHR dataset that is retrospective and longitudinal in nature. Meaning one person has multiple measurements across multiple time points (in the past). I did post [here][1] but couldn't get any response. So, posting it here

This dataset contains information about patients' diagnosis, mortality flag, labs, admissions, and drugs consumed, etc.

Now, if I would like to find out predictors that can influence mortality, I can use logistic regression (whether the patient will die or not).

But my objective is to find out what are the predictors that can help me predict whether a person will **die in the next 30 days** or **the next 240 days**, how can I do this using ML/Data Analysis techniques?

In addition, I would also like to compute a score that can indicate the likelihood that this person will die in the next 30 days? How can I compute the scores? Any tutorials links on how is this score derived?, please?

Can you please let me know what are the different analytic techniques that I can use to address this problem and different approaches to calculate score?

I would like to read and try solving problems like this


  [1]: https://stats.stackexchange.com/questions/497205/how-to-predict-an-event-for-different-time-intervals-and-compute-score