I have data set that has data for patients:
Arrival_Date : is when the patient has arrived
Seen_By_Nurse : is number of minutes patient take to be seen by nurse since arrival when value is NaN it means patient is still waiting and has not been seen by Nurse yet
Seen_By_Dr: is number of minutes patient take to be seen by dr since arrival when value is NaN it means patient is still waiting and has not been seen by Dr yet
Upset_Patient : Is this patient upset or not 1 or 0
The data look like this:
Arrival_Date Seen_By_Nurse Seen_By_Dr ... many other fields ... Upset_Patient 14/8/19 14:15 10 25 1 14/8/19 15:32 13 NaN 0 14/8/19 15:35 16 NaN 1 14/8/19 15:39 NaN NaN 0 14/8/19 15:44 NaN NaN 1
My question is: I know that working with NaN failing many predictive models. Therefore, I have to either fill it with other values, such as mean. Which is going to be wrong in my case. Or delete it which also going to be wrong in my case as the majority has not been seen by Dr or Nurse yet.
Should I fill it with big number such as 9999? Are there any other approaches?
What is the best way to deal with NaN in this case?