Create a model that predicts an event based on other time series events and properties of an object

Question

I have the following data:

• Identifier of a person
• Days in location (starts at 1 and runs until event)
• Age of person in months at that time (so this increases as the days in location increase too).
• Smoker (boolean), doesn't change over time in our case
• Sex, doesn't change over time
• Fall (boolean) this is an event that may never happen, or can happen multiple times during the complete period for a certain person
• Number of wounds: (this can go from 0 to 8), a wound mostly doesn't heal immediately so it mostly stays open for a certain period of time
• Event we want to predict (boolean), only the last row of a person will have value true for this

I have this data for 1500 people (in total 1500000 records so on average about 1000 records per person). For some people the event I want to predict takes place after a couple of days, for some after 10 years. For everybody in the dataset the event will take place, so the last record for a certain identifier will always have the event we want to predict as 1.

I'm new to this and all the documentation I have found so far doesn't demonstrate time series for multiple persons or objects. When I for example split the data in the machine learning studio, I want to keep records of the same person over time together.

Would it be possible to feed the system after the model is trained with new records and for each day that passes it would give the estimate of the event taking place in the next 5 days?

Edit: sample data of 2 persons: http://pastebin.com/KU4bjKwJ

Answer 1

sounds like very similar to this sample:

https://gallery.cortanaintelligence.com/Experiment/df7c518dcba7407fb855377339d6589f

Unfortunately there is going to be a bit of R code involved. Yes you should be able to retrain the model with new data.