Novel secondary ion mass spectrometry identification system for organic materials using random forest
Tetsuya Masuda,
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Miya Fujita,
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Tomikazu Ueno
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et al.
Journal of Vacuum Science & Technology A Vacuum Surfaces and Films,
Journal Year:
2025,
Volume and Issue:
43(2)
Published: Feb. 14, 2025
The
interpretation
of
time-of-flight
secondary
ion
mass
spectrometry
(ToF-SIMS)
data
is
often
complicated
because
ToF-SIMS
has
a
high
sensitivity
for
detecting
extremely
low
amounts
molecules
and
generally
produces
numerous
types
fragment
ions
from
each
molecule.
Although
machine
learning
techniques
have
been
applied
to
such
complex
classify
the
components
in
sample,
identifying
unknown
difficult,
even
after
classification
or
segmentation
datasets.
We
developed
new
(SIMS)
identification
system
based
on
full
spectra
by
applying
supervised
method,
random
forest
(RF),
with
effective
teaching
information
express
common
organic
molecules.
automatically
extracted
chemical
structures
material
string-converted
using
simplified
molecular-input
line-entry
system.
32
molecules,
including
peptides,
polymers,
biomolecules
as
cellulose,
were
used
training
dataset,
these
correctly
predicted
SIMS
importance
RF
indicated
that
peaks
representing
detected
materials
identified
essential
target
Moreover,
Styrofoam-like
Ocean
plastic
samples
polystyrene
This
study
demonstrates
potential
our
accurately
identify
spectra,
offering
robust
approach
expanding
molecular
samples.
Language: Английский
Machine learning-based Diagnostic model for determining the etiology of pleural effusion using Age, ADA and LDH
Qingyu Chen,
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Shu-Min Yin,
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Ming‐Ming Shao
No information about this author
et al.
Respiratory Research,
Journal Year:
2025,
Volume and Issue:
26(1)
Published: May 2, 2025
Classification
of
the
etiologies
pleural
effusion
is
a
critical
challenge
in
clinical
practice.
Traditional
diagnostic
methods
rely
on
simple
cut-off
method
based
laboratory
tests.
However,
machine
learning
(ML)
offers
novel
approach
artificial
intelligence
to
improving
accuracy
and
capture
non-linear
relationships.
A
retrospective
study
was
conducted
using
data
from
patients
diagnosed
with
effusion.
The
dataset
divided
into
training
test
set
ratio
7:3
6
algorithms
implemented
diagnosis
Model
performances
were
assessed
by
accuracy,
precision,
recall,
F1
scores
area
under
receiver
operating
characteristic
curve
(AUC).
Feature
importance
average
prediction
age,
Adenosine
(ADA)
Lactate
dehydrogenase
(LDH)
analyzed.
Decision
tree
visualized.
total
742
included
(training
cohort:
522,
220),
397
(53.3%)
malignant
(MPE)
253
(34.1%)
tuberculous
(TPE)
cohort.
All
models
performed
well
MPE,
TPE
transudates.
Extreme
Gradient
Boosting
Random
Forest
better
above
0.890,
while
K-Nearest
Neighbors
Tabular
Transformer
TPE,
0.870.
ADA
identified
as
most
important
feature.
ROC
model
outperformed
those
conventional
thresholds.
This
demonstrates
that
ML
ADA,
LDH
can
effectively
classify
effusion,
suggesting
ML-based
approaches
may
enhance
decision-making.
Language: Английский