
Deep Patient Predicts Patients Future from Health Records - jacquesm
https://www.nature.com/articles/srep26094
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grizzles
This was a collaboratively written review paper, published on github with
submissions encouraged in the form of pull requests, with co-author credit
given to anyone whose contributions met the ICJME standards of authorship.
Very cool.

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.

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jacquesm
I found this comment at least as interesting as the paper, but I can't find
the github repo for it, do you have a link?

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grizzles
Oops, it's a different paper. When I was googling Deep Patient a few months
ago I must have got confused because they mention the Deep Patient paper in
their issue tracker:

[https://github.com/greenelab/deep-
review/issues/63](https://github.com/greenelab/deep-review/issues/63)

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salimmadjd
How do we know if this is really predicting vs. identifying undiagnosed
diseases?

 _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.

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rpedela
Why would there be legal ramifications?

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imaadrashied
I'd say (at least) three ways:

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.

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jobigoud
Does your point 2 exist today with human doctors? Can you sue after getting a
second opinion?

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dth_omn_str
And couldn't it also make a wrong diagnosis? Who do you sue in that case?

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EGreg
How would you know if it made a wrong prediction? It would take a lot of
sampling and time to show it.

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aabajian
This is quite interesting. The obvious business case is for missed-diagnoses
reimbursement. U.S. hospital reimbursement is based on a monstrous coding
system (e.g. ICD-10). A subset of these codes are Hierarchical Condition
Categories (HCCs). Hospitals can bill based on the constellation of HCCs that
apply to a patient (e.g. morbid obesity, rheumatoid arthritis, COPD, etc.).
Their system could identify all HCCs for each patient and submit missed HCCs
for reimbursement.

Edit: This is not a new idea, there are lots of companies already doing this
using natural language processing.

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akras14
I am workng for one, we are called Apixio and we are hiring if anybody is
interested.

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.

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knicholes
I can't wait to receive a letter in the mail saying, "If you don't start
eating more grapes, you'll develop testicular cancer in six years."

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jacquesm
I'll be happy to send it if that's what it takes to get you to eat more
grapes.

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Noseshine
Grapes are high sugar fruit. There are others you might want to eat more of,
but limit the sweet stuff.

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PNWChris
It's an interesting result, but it's important to note there are likely
further developments in this field of study (the paper was published in May
2016). Thanks to the age, there's some really good Reddit threads [0][1]!

[0], (2016):
[https://www.reddit.com/r/MachineLearning/comments/4jtfgh/dee...](https://www.reddit.com/r/MachineLearning/comments/4jtfgh/deep_patient_an_unsupervised_representation_to/?st=j1mqz6sw&sh=060829e0)

[1], (3 days ago):
[https://www.reddit.com/r/MachineLearning/comments/65aoos/p_d...](https://www.reddit.com/r/MachineLearning/comments/65aoos/p_deep_patient_an_unsupervised_representation_to/?st=j1mr0vga&sh=4b49ddf6)

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danieljohnson
YES!

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.

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veli_joza
It would be interesting if people are already starting to gather as much data
about themselves that will relevant in few years when similar tech is widely
available. For example, taking a photo of each of your meals might prove to be
good investment of your time down the line.

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EGreg
We need something like this but with natural diet recommendations for how to
stave off diseases in each individual case. So instead of guessing, people can
be more informed about what to eat.

Having said that, how do you train it when you can't run billions of trials
internally like you can with chess?

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tgb29
I like the paper's emphasis on pre-processing the data. Once the data is
properly organized, I have no doubt that a variety of ML algorithms can be
applied to reveal different insights.

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bitwize
We all know what this will be used for in Trump's America -- to disqualify
people from health care coverage and even employment.

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Gatsky
Ok, perhaps this is needlessly inflammatory, but this comment actually
highlights an important issue. Be careful what you wish for. These approaches
require data sharing and centralisation to work well. It could be used to sell
data centralisation to health care providers, to get access to advanced
algorithms.

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.

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Florin_Andrei
Interesting research, but terrible name for the project.

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Gatsky
So this is patient2vec?

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cosmolev
predicts but refuses to explain anything

