Question: I am working on [this code](https://github.com/nfmcclure/tensorflow_cookbook/blob/master/09_Recurrent_Neural_Networks/02_Implementing_RNN_for_Spam_Prediction/02_implementing_rnn.py) for spam detection using recurrent neural networks. 

Question 1. I am wondering whether this field (using RNNs for email spam detection) worths more researches or it is a closed research field. 

Question 2. What is the oldest published paper in this field? 

Quesiton 3. What are the pros and cons of using RNNs for email spam detection over other classification methods?

Answer: 
There's a strong sentiment towards the idea that medical diagnosis is largely [Abductive Reasoning](https://en.wikipedia.org/wiki/Abductive_reasoning). See [this presentation](https://blogs.kent.ac.uk/jonw/files/2015/04/Aliseda2012.pdf) for additional details. One approach to automated abductive reasoning is [parsimonious covering theory](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2244953/).

If you want a relatively in-depth look at all of this, check out the book [Abductive Inference Models for Diagnostic Problem-Solving](http://rads.stackoverflow.com/amzn/click/0387973435) . 

Expert systems have also been shown to work very well for medical diagnosis. As far back as the 1970's, systems like [MYCIN](https://en.wikipedia.org/wiki/Mycin) could beat human experts in terms of diagnosis and treatment plans. 

From what I can tell, there doesn't seem to be any reason in principle to think that AI/ML can't be used across the board for medical diagnosis, mental health or otherwise.
You may infer some mental problems using text processing. Any text written by an individual contains a lot of clues about their mental state, probably including any health problems. 

However, I believe pursuing this would prove to be difficult due to lack of ground truth. Not many people makes it public that they have mental problems and without a reliable database it is quite difficult to make research on the field.