Variable time units LSTM
[Wow.A Great problem]

Possible solution:(Personal opinion)

First Opinion is: Use SVM or KNN instead of a neural net as your data is trivial like 2D vector.

Second Opinion is:

As you aren't trying to predict the next text possibility of a particular patient so in this case RNN(LSTM,GRU) might not help you. So a simple solution is Autoencoder to compress the data and then run a simple logistic regression or svm or may be a neural net.

How?

1.As your Patient numbers are not same in all cases so you should represent his/her all medical test data into a single vector.That is where Autoencoder comes into play.example:

Suppose we have 3 patient: First-3 test , second-2 test ,three-5 test. Our goal is to compress each patient data into 2 dimension vector(as your features are RBC and WBC , 2 features , so it will learn well if we compress it into 2D vector).

(inside asterik sign(*) your data goes)

First Patient=[[*],[*],[*]] # three test
Second Patient=[[*],[*]] # two test
Third Patient=[[*],[*],[*],[*],[*]] # five test


Now you compress the data via Variational autoencoder. 

scale your data as wbc>>rbc

then use variational autoencoder to get the latent space vector

use dimesion=2 in latent space via Dense(2)

now when you get the vectors just add the vectors of test for each patient

Like : For First patient you got three vector in latent space like-

[2.33,4.2]+[5.11,9.22]+[0.21,6.32]=result..and You got the representaion for First patient.

after you got all of them a trivial svm/polynomial regression would be just fine.

Let me know how you solved it.