[itschekkers]

I hate to pour cold water on this, but medical researchers have been predicting mortality for years.

For example, here's a paper from the 1980's also predicting when a patient will die, also using a couple of thousands of patients' data: http://europepmc.org/abstract/med/3816253

And, Google's paper wasn't published in Nature, it was in a new open access journal owned by Nature. In academia, that is like the difference between a really fancy porsche, and a really cheap volkswagen (two very different things, both owned by the same company).

[himom]

Regardless of medical / health insurance purposes, actuaries have been doing this for over a century esp. to price life insurance, among other things.

[joker3]

Google's innovation isn't predicting mortality. It's predicting mortality based on data that was hidden from previous models. That's very likely where all of the improvement is coming from, but since it's hard to write a good headline or lede based on that, we get this instead.

[muraiki]

From the paper:

"...the novelty of the approach does not lie simply in incremental model performance improvements. Rather, this predictive performance was achieved without hand-selection of variables deemed important by an expert, similar to other applications of deep learning to EHR data. Instead, our model had access to tens of thousands of predictors for each patient, including free-text notes, and identified which data were important for a particular prediction."

So it sounds like the advance here is actually in the following: "a generic data processing pipeline that can take raw EHR data as input, and produce FHIR outputs without manual feature harmonization".

The article doesn't explain this very clearly. Yay!

[carlmr]

>In academia, that is like the difference between a really fancy porsche, and a really cheap volkswagen (two very different things, both owned by the same company).

I think you want to say Skoda or Seat. Both owned by VW and the cheap cars.