In this lecture, we discuss the idea
of predictive analytics in medicine.
Specifically, we introduce the idea
of using clustering methods for better predicting heart
attacks.
Heart attacks are a common complication of coronary heart
disease, resulting from the interruption of blood supply
to part of the heart.
Heat attack is the number one cause
of death for both men and women in the United States.
About one in every four deaths is due to heart attack.
A 2012 report from the American Heart Association
estimates about 715,000 Americans have a heart attack
every year.
To put this number into perspective,
this means that every 20 seconds, a person
has a heart attack in the United States.
It is also equivalent of September the 11th
repeating itself every 24 hours, 365 days a year.
Nearly half of these attacks occur
without prior warning signs.
In fact, 250,000 Americans die of sudden cardiac death
yearly, which means 680 people every day die
of sudden cardiac death.
A heart attack has well-known symptoms-- chest pain,
shortness of breath, upper body pain, nausea.
The nature of heart attacks, however,
makes it hard to predict, prevent, and even diagnose.
Here are some statistics.
25% of heart attacks are silent.
47% of sudden cardiac deaths occur outside hospitals,
suggesting that many patients do not act on early warning signs.
Only 27% percent of respondents to a 2005 survey
recognized the symptoms and called 911 for help.
How can analytics help?
The key to helping patients is to understand
the clinical characteristics of patients
in whom heart attacks was missed.
We need to better understand the patterns in a patient's
diagnostic history that link to heart attack
and to predicting whether a patient is
at risk for a heart attack.
We'll see, in this lecture, how analytics
helps to understand patterns of heart attacks
and to provide good predictions that in turn lead to improved
monitoring and taking action early and effectively.