♪ I love it when you call me big data ♪
Welcome to The Dr. Data Show!
I'm Eric Siegel.
Various organizations predict when you'll die.
Insurance companies, police departments, doctors,
and in some cases, this actually helps you.
The fact is, there are computers out there
calculating the probability you will die
in the relatively near future.
These predictions serve a variety of purposes,
such as trying to medically allay your demise,
or at least administratively manage it.
Even Facebook has applied for a patent
that covers predicting if one of your friends has died,
even if their profile hasn't been updated
to reflect that yet.
Death prediction is a very morbid topic indeed
but a very practical one.
So how does it work?
What technological sorcery is at play?
And how accurate is it?
And, gee whiz, what's it used for?
In the next several minutes, I shall answer these questions
and cover five reasons computers predict when you will die.
It's a defining characteristic of life
that we're all gonna die.
We're gonna kick the bucket, we're gonna buy the farm,
bite the dust, and croak.
People do try to gloss over this little detail.
For example, the publisher of my book,
Predictive Analytics, was initially quite resistant
to authorize the book's subtitle
'cause it has the word die in it.
The book's title is an informal definition
of the field, by the way.
The full title is, Predictive Analytics:
The Power to Predict Who Will Click, Buy, Lie, or Die.
After the book published, IBM offices in Australia
even disallowed the book's full title
in their printed materials because of that last word.
But we shouldn't gloss over death,
not only for the obvious reason of its unavoidability,
but also 'cause death matters.
I mean, every important thing a person does
can be valuable to predict for all kinds
of government, business, and healthcare operations.
And this definitely applies for the final thing
we will each do.
To put it gently, I'm talking about,
you know, keeling over.
How does it work?
Well, the same machine learning methods
apply for this use of predictive analytics
as for predicting any other kind of behavior or outcome.
What makes the difference for what it's predicting
is just the training data.
For predicting death, we supply data
that includes examples of A, people who died
say, within a time window of 12 months,
and examples of B, people who did not die
within that time frame, so that our trusty machine
can derive patterns that discern group A from group B.
Practically speaking, for the machine to learn
is for it to find trends in how those two groups differ.
The basis for differentiating these two groups
will be any and all things recorded about individuals,
including their demographics and their previous behavior.
Whichever such attributes are included
in the training data end up serving as inputs
to the predictive model.
For example, in healthcare this can be all kinds
of clinical features, test results, and even MRIs.
But then it goes beyond all that
to some pretty surprising stuff.
All kinds of unexpected factors
you wouldn't immediately think of might help predict.
For example, early retirement is a risk factor.
For a group of Austrian men in one study,
early retirement decreased life expectancy.
And, on the boat the Titanic in 1912,
women were almost four times more likely
to survive than men.
Rock stars die younger, and solo rock stars die
even younger than those in bands,
according to public health offices in the U.K.
Now, if you'd like to quickly calculate
your own fate, death-clock.org will estimate
the Grim Reaper's ETA by way of statistics
from the World Health Organization,
it just asks for a handful of elements
like age, gender, body-mass index,
and cigarette and alcohol consumption.
For me, it says I've got until December 20th, 2052.
But that's far from certain.
As with most outcomes we may wish to predict,
death is impossible for anyone or any technology
to predict with high accuracy in general.
But it's still useful.
I should be quick to point out that,
like many other outcomes,
it's definitely possible for machine learning
to predict death considerably better than guessing,
and often better than human experts.
I'll explain why that limited level of prediction
is still extremely useful, but first a little more
on what it means to estimate these probabilities.
The trends learned from data are together known
as a predictive model.
For death prediction, this is often called
a mortality model.
Or, the more genteel among us might call it a croakometer.
Just kidding.
Now, when the model predicts for an individual person today
whether they'll live or die, only time will tell for sure
if it turns out to be correct.
To be more specific, some models calculate,
for example, the probability you will die within,
say, one year.
So, among those assigned around a, say,
30% probability, about 30% of those
will indeed die within a year.
But we don't have certainty about any one of them
as an individual, on a case-by-case basis,
we don't know for sure one way or the other.
You can think of probabilities as just a level
of predictive confidence that death will happen,
a risk level, which is basically never full on 100%.
There's never total certainty, always some shade of gray.
Now, just to clarify, even though the model
just gives us probabilities,
the word prediction does still apply.
If you say, Let's put our money on everyone
assigned a 90% or greater risk and bet they'll die.
Then we're using the model to predict,
and in fact we may be quite accurate
for that particular smaller segment
of high risk individuals, but predictions
for the overall entire population in general
won't be accurate per se.
So beware any claims of high accuracy in machine learning.
It's not a magic crystal ball.
So by the way, all these points
also apply for all predictive objectives,
for any other outcome or behavior you're trying to predict,
be it predicting who will click, who will buy,
who will lie, or who will eat pie.
Anyway, predicting death, even just better than guessing,
can be super valuable.
Here are five ways machine learning
for death prediction is put to good use.
Number one: Healthcare providers predict death
to help prevent it.
Patients with a high mortality risk are flagged accordingly,
and this guides clinical treatment decisions.
For example, Riskprediction.org.uk,
you can go check it out,
predicts the risk of death in surgery,
based on characteristics of the patient
and the type of surgery.
Number two: End-of-life counseling, palliative care,
and hospice care can be more effectively targeted
when we know which patients
are the most likely to pass away.
Many who are nearing the end of life
will greatly benefit from these offerings,
especially if well-timed.
For this reason,
there's an active trend among healthcare institutions,
insurance companies, and research labs
to apply machine learning for this purpose.
Number three: Suicide prevention.
The VA, the Crisis Text Hotline, and psychiatric research
at large work to flag which individuals,
be they veterans or those who've contacted
a crisis hotline, are at the greatest risk
in order to improve triage
and more precisely target intervention.
Number four: Law enforcement and the military
predict kill victims in order to protect.
For example, researchers assess the risk
to individual soldiers, such as when they're parachuting,
and law enforcement in, for example,
Maryland applies predictive models
to detect which inmates are more at risk
to be either perpetrators or victims of murder.
And finally, number five: Life insurance.
Now, the central thing life insurance companies do
is predict life expectancy, that is,
they predict when you're going to die.
That's how they set premiums and manage coverage.
To improve their predictions, life insurance companies
more and more these days go beyond conventional
actuarial look-up tables and integrate
machine learning methods in order
to improve predictive performance.
And, hold on, we're not quite finished yet,
as a special bonus, there's actually one more thing
to predict even after death.
The Chicago Police Department predicts
whether a murder can be solved,
based on the characteristics of a homicide and its victim.
The objective is to better manage and triage investigations,
which improves overall enforcement, which, one hopes,
could help reduce the number of future murders.
And that wraps up another deadly episode
of the Dr. Data Show.
I'm Eric Siegel, thanks for watching.
Hit like and share this video if you think
your friends would also be interested
in the very morbid but practical topic of death prediction.
And for access to the entire web series,
go to TheDoctorDataShow.com.
♪ Who's your data? ♪
♪ provide me the data to improve ♪
♪ And I'll apply the computation ♪
♪ I love it when you call me big data ♪
♪ Predictive analytics can help you with decisions ♪
♪ You call, mail, credit or hire with precision ♪
♪ On law, love, and life you can prognosticate ♪
♪ Who to investigate, incarcerate ♪
♪ Set up on a date or medicate ♪
♪ Charlie Brown never gets his kicks ♪
♪ That's why every old dog needs a brand new trick ♪
♪ If you get sick of chasing sticks ♪
♪ Or clicks with just a quick fix ♪
♪ You need to learn to predict ♪
♪ I can predict your every move ♪
♪ Just give me all your information ♪
♪ Who's you data? ♪
♪ Provide me the data to improve ♪
♪ And I'll apply the computation ♪
♪ I love it when you call me big data ♪
For more infomation >> Lari White - Stand By Your Man / You Can't Go Home Again (George Jones Show) - 1999 - Duration: 14:04. 
For more infomation >> Martina McBride - I Love You / Love's The Only House / Anything & Everything (QVC) - 1999 - Duration: 16:20. 
Không có nhận xét nào:
Đăng nhận xét