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A random Abstract
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
"large number"
symptom
acute rheumatic fever
adrenal incidentaloma
allergic bronchopulmonary aspergillosis
canavan disease
coats disease
cowden syndrome
dedifferentiated liposarcoma
dracunculiasis
epidermolysis bullosa simplex
fabry disease
familial mediterranean fever
gm1 gangliosidosis
heparin-induced thrombocytopenia
hereditary cerebral hemorrhage with amyloidosis
hirschsprung disease
kindler syndrome
legionellosis
malignant atrophic papulosis
neuralgic amyotrophy
phenylketonuria
pleomorphic liposarcoma
primary effusion lymphoma
scrub typhus
severe combined immunodeficiency
triple a syndrome
waldenström macroglobulinemia
well-differentiated liposarcoma
wiskott-aldrich syndrome
wolf-hirschhorn syndrome
x-linked adrenoleukodystrophy
zellweger syndrome
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