Rare Diseases Symptoms Automatic Extraction
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A random Abstract
Our Project
Our Team
Utilizing a language model to improve online dynamic data collection in P300 spellers.
[locked-in syndrome]
P
300
spellers
provide
a
means
of
communication
for
individuals
with
severe
physical
limitations
,
especially
those
with
locked-
in
syndrome
,
such
as
amyotrophic
lateral
sclerosis
.
However
,
P
300
speller
use
is
still
limited
by
relatively
low
communication
rates
due
to
the
multiple
data
measurements
that
are
required
to
improve
the
signal-
to
-noise
ratio
of
event-related
potentials
for
increased
accuracy
.
Therefore
,
the
amount
of
data
collection
has
competing
effects
on
accuracy
and
spelling
speed
.
Adaptively
varying
the
amount
of
data
collection
prior
to
character
selection
has
been
shown
to
improve
spelling
accuracy
and
speed
.
The
goal
of
this
study
was
to
optimize
a
previously
developed
dynamic
stopping
algorithm
that
uses
a
Bayesian
approach
to
control
data
collection
by
incorporating
a
priori
knowledge
via
a
language
model
.
Participants
(
n
=
17
)
completed
online
spelling
tasks
using
the
dynamic
stopping
algorithm
,
with
and
without
a
language
model
.
The
addition
of
the
language
model
resulted
in
improved
participant
performance
from
a
mean
theoretical
bit
rate
of
46
.
12
bits
/
min
at
88
.
89
%
accuracy
to
54
.
42
bits
/
min
(
)
at
90
.
36
%
accuracy
.
Diseases
Validation
Diseases presenting
"amyotrophic lateral sclerosis"
symptom
adrenomyeloneuropathy
alexander disease
cadasil
dystrophic epidermolysis bullosa
inclusion body myositis
liposarcoma
locked-in syndrome
neuralgic amyotrophy
pyruvate dehydrogenase deficiency
sneddon syndrome
triple a syndrome
x-linked adrenoleukodystrophy
This symptom has already been validated