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.
It's already being done, and apparently with very good results.

See: [Predicting Risk of Suicide Attempts Over Time Through Machine Learning](http://journals.sagepub.com/doi/abs/10.1177/2167702617691560?journalCode=cpxa&), Walsh, Ribiero, Franklin

Here is the abstract from the paper:

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> Traditional approaches to the prediction of suicide attempts have limited the accuracy and scale of risk detection for these dangerous behaviors. We sought to overcome these limitations by applying machine learning to electronic health records within a large medical database. Participants were 5,167 adult patients with a claim code for self-injury (i.e., ICD-9, E95x); expert review of records determined that 3,250 patients made a suicide attempt (i.e., cases), and 1,917 patients engaged in self-injury that was nonsuicidal, accidental, or nonverifiable (i.e., controls). We developed machine learning algorithms that accurately predicted future suicide attempts (AUC = 0.84, precision = 0.79, recall = 0.95, Brier score = 0.14). Moreover, accuracy improved from 720 days to 7 days before the suicide attempt, and predictor importance shifted across time. These findings represent a step toward accurate and scalable risk detection and provide insight into how suicide attempt risk shifts over time.
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This has gotten national press attention quite recently. Here are a couple of prior articles on the result and endeavor:

[Artificial Intelligence is Learning to Predict and Prevent Suicide](https://www.wired.com/2017/03/artificial-intelligence-learning-predict-prevent-suicide/) (Wired)

[Artificial intelligence can now predict suicide with remarkable accuracy](https://qz.com/1001968/artificial-intelligence-can-now-predict-suicide-with-remarkable-accuracy/) (Quartz)

Is strongly suspect ML can be used in an array of application related to human mental health. For instance, it is entirely possible a app would be able to discern the mood swings in people with bipolar disorder based on activity or lack thereof.