
The Emerging Field of Health Data Science in Boston - re_jena
http://insighthealthdata.com/blog/blog1_EmergingField/blog1_emerging_field.html
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sandworm101
Strapping a fitbit to everyone so docs can better monitor their life choices
reminds me of the logic behind a classic Simpsons scene: A more precise
measurement of an already accurately-understood condition does not equate to
greater understanding of how to actually fix the problem.

"Marge: Can't you do something for him?

Dr. Hibbert: Well, we can't fix his heart, but we can tell you exactly how
damaged it is.

Homer: What an age we live in."

~~~
mburke05
I think it's a little more optimistic than that. Recently there was a Kaggle
competition that analysed enzyme make-up of saliva of animals in order to
predict epileptic seizures (I might be mincing specifics here but the point
remains). So while it's true that we won't be able to treat some of the things
that a fitbit etc. highlights (bad decision making, cancer, etc.) it can
definitely potentially be used in preventative diagnoses and early detection
warning systems. What an age we live in indeed!

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brandonb
I worked with a couple of the Insight Data Fellows mentioned in this blog
post, on the UCSF Health eHeart study. Mike Klein (Fitbit data to predict
heart failure) and Yancheng Liu (Apple Watch data to predict atrial
fibrillation) both did a fantastic job!

If anybody here has questions about the program or the projects, happy to give
the "customer's" point of view on what it's like to work with Insight.

~~~
teekert
A bit late but can you tell me how exactly walking patterns change when you
are about to have heart attack? I think it is all meaningless without a
described mechanism that can be tested in a scientific way. Big data analyses
are nice to aid in hypothesis generation but until a testable mechanism behind
the observation is described, to me the "mechanism" is just as likely to be
over fitting as real. The FDA also wants mechanistic explanations before
approving applications.

When looking at enough parameters, there are always parameter subsets that
nicely match the phenomenon you are looking for. Whether that is a heart
attack or a running nose.

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entee
I don't think data science for health is in any way limited to wearables.

I think this is the first time we've had the opportunity to do real data
science at a massive scale with patient data of all sorts, including
electronic medical record (EMR) data. This is because we now have:

\- Widespread use of EMRs

\- Decent standardized schemas for representing that data (i.e. FHIR
[https://www.hl7.org/fhir/](https://www.hl7.org/fhir/))

\- Not-terrible medical ontologies (i.e. RxNorm for drugs, LOINC for labs,
Snomed for concepts etc.)

\- Reasonably accessible tools to process the massive datasets (<insert big
data toolkit here>)

Many of these things emerged or matured fairly recently, so I think we're
going to see a real explosion in medically relevant data science.
Fitbit/wearable sensor data is only the beginning, I think things like
meaningfully informing diagnoses of patients based on medical history beyond
what a doc or nurse could do on their own is within reach, and the sky's the
limit!

(Full disclosure I work for a health DS startup, and was an Insight Fellow,
although prior to the health program existing)

~~~
gmarx
We're getting there. We still have huge problems on the data input side.
Standards like fhir might be okay (though limited). Problem is most of the
data we would love to analyze is never input as discrete data in the first
place. We are far too dependent on the free text note. Also, such data that is
available tends not to be available through, say, an API call, as a FHIR
message...yet

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shad0wfax
I think we're just scratching the surface when it comes to the types of health
data we can retrieve. There is a lot of power in bringing together data sets
from the sensors/apps (more of a continuous feed of wellness data) and
correlating that with discrete medical data models.

The startup I work at is stitching together all of this data to gain a better
profile or health state of a person, and it's pretty amazing some of the early
insights we've been able to uncover.

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melling
"What if you could pick out early warning signs of heart conditions out of
somebody's Fitbit data? It turns out that you can. "

Yes, in the future everyone will wear a smartwatch:

[https://h4labs.wordpress.com/2015/07/28/in-the-future-
everyo...](https://h4labs.wordpress.com/2015/07/28/in-the-future-everyone-
will-wear-a-smartwatch/)

~~~
aheilbut
What if you could pick out early warning signs of heart conditions just by
looking at somebody and observing that they are overweight? It turns out that
you can.

~~~
melling
So, you're arguing that measuring other data points doesn't add value? Just
look at someone and if they are overweight that's enough of a data point?

~~~
aheilbut
It's an empirical question. I would predict that you could do pretty darn well
with height, weight, resting heart rate, blood pressure, bloodwork, and asking
the patient about their physical activity. Fitness trackers are cool, but I'm
just arguing against breathless health claims without justification.

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Sexagesimal
This is going to have a huge impact especially when combined with human
longevity data science directions.

~~~
meeper16
[http://calicolabs.com](http://calicolabs.com)

[http://genopharmix.com](http://genopharmix.com)

[http://humanlongevity.com](http://humanlongevity.com)

