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In this lecture,
we discuss the idea

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of predictive
analytics in medicine.

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Specifically, we
introduce the idea

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of using clustering methods
for better predicting heart

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attacks.

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Heart attacks are a common
complication of coronary heart

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disease, resulting from the
interruption of blood supply

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to part of the heart.

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Heat attack is the
number one cause

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of death for both men and
women in the United States.

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About one in every four
deaths is due to heart attack.

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A 2012 report from the
American Heart Association

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estimates about 715,000
Americans have a heart attack

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every year.

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To put this number
into perspective,

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this means that every
20 seconds, a person

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has a heart attack
in the United States.

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It is also equivalent
of September the 11th

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repeating itself every 24
hours, 365 days a year.

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Nearly half of
these attacks occur

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without prior warning signs.

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In fact, 250,000 Americans
die of sudden cardiac death

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yearly, which means 680
people every day die

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of sudden cardiac death.

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A heart attack has well-known
symptoms-- chest pain,

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shortness of breath,
upper body pain, nausea.

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The nature of heart
attacks, however,

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makes it hard to predict,
prevent, and even diagnose.

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Here are some statistics.

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25% of heart attacks are silent.

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47% of sudden cardiac deaths
occur outside hospitals,

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suggesting that many patients do
not act on early warning signs.

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Only 27% percent of
respondents to a 2005 survey

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recognized the symptoms
and called 911 for help.

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How can analytics help?

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The key to helping
patients is to understand

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the clinical
characteristics of patients

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in whom heart
attacks was missed.

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We need to better understand
the patterns in a patient's

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diagnostic history that
link to heart attack

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and to predicting
whether a patient is

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at risk for a heart attack.

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We'll see, in this
lecture, how analytics

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helps to understand
patterns of heart attacks

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and to provide good predictions
that in turn lead to improved

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monitoring and taking action
early and effectively.