I'm a bit disappointed as I only found out about this a month or so ago. I would have loved to contribute to it, if only because of how the paper was written. Just fantastic.
For each patient we considered only the prediction of novel diseases, discarding the re-diagnosis of a disease
It seems to me you need a longer time series (patient with 3,4,5 years of medical history) to be able to predict a future disease state. And I don't believe I saw that in the article (I admit I read through it quickly).
If it turns out, this is just identifying undiagnosed diseases, I wonder if this research will have any legal ramifications.
From what I have skimmed they do not do this in the paper, although to be fair this would probably not be practical because it would require more tests to have been done in the past which in many cases were not done, e.g. imaging to confirm absence of a cancer at the time of prediction.
That said, I have worked on thia same problem and also lazily call it prediction. Also there is still immense value in earlier diagnosis in cases where early treatment leads to better outcomes, like in sepsis or cancer.
1) Docs using it to double check themselves before diagnosing patients, thereby, hopefully, limiting their liability.
2) Patients getting second opinions from Deep Patient and suing the previous doc for not catching a disease.
3) Patient getting diagnosed at a very late stage of an illness and patient + family thinking earlier docs should've caught disease. Hire lawyer, lawyer runs through Deep Patient simulation, Deep Patient identifies illness, DP identification used to prosecute earlier docs. I guess kinda like 2.
Edit: This is not a new idea, there are lots of companies already doing this using natural language processing.
We have access to one of the cooliest data sets ever - health records of millions of patians, and we too are aiming to predict conditions (among other things) and save lives.
Our corp website is crap, but we are doing interesting things out there.
, (2016): https://www.reddit.com/r/MachineLearning/comments/4jtfgh/dee...
, (3 days ago): https://www.reddit.com/r/MachineLearning/comments/65aoos/p_d...
It seems that you need more, and more complete information to do deep learning on records, but it also seems this is a first small step into centralizing and using health records for public health.
Having said that, how do you train it when you can't run billions of trials internally like you can with chess?
But then the government starts using those algorithms to ration healthcare. Not contributing your data means you don't get anything. There won't be any going back then.