
Deep Learning with Electronic Health Record (EHR) Systems - practicalAI
https://practicalai.me/blog/deep-learning-with-ehr-systems
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oldgradstudent
That would a terrible idea.

Here is a simple example why:

Finasteride is a compound that is used in two drugs. Proscar is used for
prostate enlargement. It is old, out-of-patent, and has cheap generics.
Propecia is used for hair loss. It is a newer, and (at the time) very
expensive. The only difference is that Propecia is a lower-dose formulation.

What people did was to ask their doctors to perscribe generic Proscar, and
then break the pills up to take for hair loss. Doctors would then justify the
prescription by "diagnosing" enlarged prostate. This would enter the patient's
health records.

If you apply deep learning without being aware of this "trick", you would
learn that a lot of young men have enlarged prostates, and that Proscar is an
effective, well-tolerated treatment for it.

Health records are often political-economic documents rather than medical.

~~~
oncooncogene
I think this article actually agrees with you. The very first NOTE is this:

""" Note: Be cautious about using data that was primarily created for
insurance purposes. Often, it's not truly reflective of patient's condition
but rather encompassing for billing / profit. Luckily, there are clinical
reports, like radiology, diagnostic imaging, pathology reports, etc., that are
intended for physician use and are more reflective of true patient conditions.
Unfortunately, most of this data is not readily available in APIs because it's
largely unstructured. This is a ripe space for ML to take raw, unstructured
data and produce structured, computable data. """

~~~
oldgradstudent
Insurance is just a one part of the problem.

Large practices have treatment standards on which physicians are evaluated.
Reporting side-effects might be politically inconvenient in some cases.
Medicine is also, like other human endeavors, subject to fashions and fads.

At best, applying machine learning to health records will generate a
hypothesis that must be checked in a properly controlled trial.

~~~
travisporter
So it’s not a “terrible idea”?

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practicalAI
Wrote this last yr. before jumping back into clinical ML but never got around
to sharing it. Added some 2019 updates (clinical BERT and approach for
industry applications).

I updated the appendix with a few papers from 2019 but it felt like there were
10X more papers compared to 2018 (which is fantastic!). But, instead of making
the appendix even longer, I highly recommend just following [http://www.arxiv-
sanity.com/search?q=health](http://www.arxiv-sanity.com/search?q=health) to
stay up-to-date.

Even if you’re not in the health space, you’ll find ingenious interpretability
techniques and tips leveraged by researchers out of necessity of being in the
clinical space. I conclude with a realist note on the challenges that lie
ahead for safely transitioning research to the clinical setting.

~~~
JusticeJuice
Great work! I've got a rather wild ML ERH story to share.

So wrote my thesis on the design of EHR system
([http://barnett.surge.sh/](http://barnett.surge.sh/)), and I interviewed this
guy who said he was working on a crazy revolutionary ML-powered EHR system. It
apparently had features such as voice recognition for writing notes instead of
typing - but it also featured taking unstructured medical notes, and with the
great AI, structuring them. So suddenly large scale medical record analysis
would be easy - a rather incredible idea. However it sounded like they were at
a very early stage, and they had nothing to show - so I thought 'yeah good
luck getting that working' and trucked along with the thesis.

A few months after finishing my thesis, a friend of mine sends me this news
article ([https://thespinoff.co.nz/the-best-of/31-12-2018/summer-
reiss...](https://thespinoff.co.nz/the-best-of/31-12-2018/summer-reissue-the-
mystery-of-zach-new-zealands-all-too-miraculous-medical-ai/)).

Turns out a journalist had heard of this medical AI, and dug way deeper. This
guy (Albi) had convinced the GP I talked to that he had a working medical AI
system. He had actually gone so far as to have the GP email 'the ai' and have
it reply. However the whole thing was a fucking sham - it didn't exist, and
the person replying was honestly probably just Albi. They were trying to raise
funding to get it further, based on this claim of a 'functional ai'.

I don't think the GP I interviewed was in on the con, I think he was being
taken for a ride. However once the article was published, there was heaps of
attention and it all kind of fizzled out, and Albi was recently bankrupted.

But, I'm glad to see somebody is working on this problem - who isn't a conman
haha.

~~~
nradov
Every major EHR vendor and some other related organizations have been working
on that problem for years. It's seen as a "holy grail" of EHR functionality.

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Bhagaban
Arent there privacy concerns?

~~~
adi4213
Good question. I imagine you would train models using the deidentified
datasets cited on the page and design your prediction pipeline in the same
vein as any other HIPAA-data related application.

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dlphn___xyz
whats the goal of this project? what are you actually trying to learn from the
EHR record?

~~~
oncooncogene
I think it's mean to be a good review on the field and what's possible.
They're also covering what you shouldn't do with EHR data and some of the
dangers

