Social Media: the Future of Diagnosis?
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Social Media: the Future of Diagnosis?


Welcome to Impact Factor, your weekly commentary
on a new medical study that is just perfect for sharing on social media. Of course, what would that say about you? A new study appearing in PLOS One suggests
that your Facebook posts can be used to diagnose a variety of diseases. Researchers from the University of Pennsylvania
found 999 brave souls willing to share their entire Facebook history – amounting to 950,000
Facebook status updates and 20 million words, which is equivalent to about 15 copies of
Proust’s Remembrance of Things Past although with slightly less sardonic wit and slightly
more emojis. From this data they derived what they call
a “social mediome” – we’ll see if that catches on – a set of 700 variables
that reflected the 500 most-common word pairs seen, and 200 common word-cluster “topics”. Each of the 999 individuals thus had a 700-variable
fingerprint that represented all that they put out there into the ether of the social
network. Linking to the electronic medical record,
the researchers asked whether they could use that fingerprint to predict the presence of
21 conditions like diabetes, psychosis, and pregnancy. And, for basically all of them, they could
with varying degrees of accuracy. Pregnancy, in fact, was the easiest to predict,
while the presence of coagulopathy was the hardest. This is probably for the best. The authors compared Facebook’s ability
to predict with the predictive ability of a combination of three demographic factors
– a individual’s age, race, and sex, finding that Facebook-based prediction was significantly
superior to demographic-based prediction for 10 of the 21 conditions. Most of the word-clusters had good face validity. People who were depressed, for example were
more likely to have posts with words like “hurt” “feelings” and “care”. Not all the clusters made so much sense. One strong predictor of the presence of diabetes
was a word cluster with words like “god”, “pray” and “lord” suggesting these
postings are capturing some data that simple demographics do not. Where does this all go? Well, the implication is that someday, by
sharing your online data with your doctor, the doctor may be able to identify you as
at risk for a condition that you didn’t even know about. Of course, this study doesn’t really go
there. There is no information as to the timing of
posts versus the diagnosis – it’s one thing to post about diabetes when you know
you have diabetes, another thing altogether to predict FUTURE diabetes from current Facebook
posts. And of course, comparing only to demographic
information is a bit of a straw man. Docs have a lot more info about patients than
just their age, sex, and race. Still, the data in your social media history
may reveal aspects of health that we don’t capture well otherwise. But of course, whether you are willing to
share that side of you with your health professional may say more about you than all those 20 million
words ever could.

One thought on “Social Media: the Future of Diagnosis?

  1. Seems like such a stretch to me.. how can the choice of words you post have an effect on whether you are at risk for diabetes or other medical diagnoses? social media is a powerful source of data useful to the medical and public health fields but I’m not so sure if it is of much use for future medical diagnoses.

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