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Our Project
Our Team
A Temporal Pattern Mining Approach for Classifying Electronic Health Record Data.
[heparin-induced thrombocytopenia]
We
study
the
problem
of
learning
classification
models
from
complex
multivariate
temporal
data
encountered
in
electronic
health
record
systems
.
The
challenge
is
to
define
a
good
set
of
features
that
are
able
to
represent
well
the
temporal
aspect
of
the
data
.
Our
method
relies
on
temporal
abstractions
and
temporal
pattern
mining
to
extract
the
classification
features
.
Temporal
pattern
mining
usually
returns
a
large
number
of
temporal
patterns
,
most
of
which
may
be
irrelevant
to
the
classification
task
.
To
address
this
problem
,
we
present
the
Minimal
Predictive
Temporal
Patterns
framework
to
generate
a
small
set
of
predictive
and
non-spurious
patterns
.
We
apply
our
approach
to
the
real-world
clinical
task
of
predicting
patients
who
are
at
risk
of
developing
heparin
induced
thrombocytopenia
.
The
results
demonstrate
the
benefit
of
our
approach
in
efficiently
learning
accurate
classifiers
,
which
is
a
key
step
for
developing
intelligent
clinical
monitoring
systems
.
Diseases
Validation
Diseases presenting
"good set"
symptom
heparin-induced thrombocytopenia
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