which deep learning text classifier is good for health data I have a data set like this:

postID  Sentence                                         drugYesOrNo
1       He went out with his friends    
2       He behaved nicely while talking with me 
3       He stopped using drugs after a while                 1
4       He did not meet any friend during last week 
1       He slowly cut usage of drugs                         1
2       He smiled like he is good   
3       He did not seem happy with his situation    


As you see there is two features. the first feature is our sentence and the second feature shows is this sentence is a sign that patient has stopped his drug or not.

the first column shows that sentence which are part of a paragraph. for example HERE sentence 1-4 are one paragraph in which we have splited them to see which sentence exactly show the stopping of drugs. 
so sentence 3 in first paragraph shows this.

In the second case, sentence 1-3 is part of a paragraph. here sentence one shows that this person stopped using drugs(which is not good the person should continue)

so my goal is to apply a deep learning text classifier on my text data and make a model and so when I received  A NEW PARAGRAPH, I will be able to predict if the person has stopped his drugs or not.

first question, with this case study, which deep learning text classifier may work best?

Secondly, as you see we have cut the paragraph in to series of the sentences. but in reality we will give a paragraph to test the model. in your idea what will be the best approach to deal with this?

the thing that came to my mind is that while testing and receiving a paragraph, we again split the paragraph to sentences and give those sentences to the model but I am not sure it is a good approach.

we have 900 of these sentences, again Im not sure with this much of data it will be goof to apply deep learning classifier on it.

I appreciate it if you give me your point of view:)

Update after reading comments

I asked a couple of guys to make such a dataset for me. I mean looking at paragraph, spliting, then saying which sentence has that meaning(stopping drugs or not). what if I did not ask them to explicitly say which sentence does have that meaning and just point which paragraph does have that meaning(stopping drugs or not). do you think labeling exactly which sentence does have that meaning has been a good idea rather than which paragraph does have that meaning? I hope I am clear enough :)
Yes, you should split the paragraph to sentences and give those sentences to the model. Your deep structure should be like this:

In the first layer, you must put a word embedding layer to represent a sentence as a sequence of vectors. In the second layer, you must put LSTM to be able to model your sequence vector as a single vector. Now, you can add successive layers with linear, relu or sigmoid activation functions to make your model deeper. In the last layer, you must use sigmoid activation function to do binary classification.