Mimic a Mahout like system
This seems to me as the plain old recommendation problem. The Accounts are the USERS and the Symptoms are the ITEMS. Each time an Account shows a particular Symptom your system will increment a count value.

Creating the following dataset:

ACCOUNT, SYMPTOM, COUNT

Now you can use/implement any sort of recommender system (Mahout is only an option, have you seen MyMediaLite) or you can even implement yours. 

Let's reuse your ideas:
* You'd like to use a user-based similarity 
* If an Account has shown 2 or more times the same symptom it seems to be important

So you can filter out the Account, Symptom pairs with less than 2 counts, and with the rest you create the following datasets:


User, Item dataset:


ACCOUNT, SYMPTOM


Table with a unique column containing all Users:


ACCOUNT


Table with a unique column containing all Items:


SYMPTOM

Now you can use directly the User-KNN algorithm from MyMediaLite.

With the recommender model already trained you can pass any ACCOUNT as input and it will give you a ranked list of the most probable SYMPTOMS that might appear.

Obs.: Initially ignore the time, then you could use it to partition your data in past/future and evaluate the recommendation in a more realistic way. ;-)