Research Square (Research Square),
Год журнала:
2025,
Номер
unknown
Опубликована: Март 18, 2025
AbstractBackground:
Taiwan
has
a
high
caesarean
section
(CS)
rate,
ranging
from
37%
to
38%.
Vaginal
Birth
After
Cesarean
(VBAC)
offers
potential
solution
reduce
these
rates.
However,
the
prevalence
of
VBAC
remains
below
0.5%,
primarily
due
concerns
about
risks
adverse
maternal
and
perinatal
outcomes.
Objectives:
This
study
aims
evaluate
predictive
performance
various
machine
learning
(ML)
models
using
pregnancy,
labor,
intervention-related
features
predict
success
support
real-time
clinical
decision-making
during
labor.
Study
Design:
This
retrospective
exploratory
analyzed
data
collected
hospital
in
northern
between
January
2019
May
2023.
Statistical
methods
included
demographic
comparisons,
feature
evaluations,
model
metrics
such
as
accuracy,
precision,
recall,
F1-score,
area
under
curve
(AUC).
SHapley
Additive
exPlanations
(SHAP)
analysis
was
used
interpret
importance
labor
progression.
Results:
A
comparison
Failure
group
(n=22)
Success
(n=33),
totaling
55
records
36
pregnant
women,
revealed
significant
differences
parity,
spontaneous
rupture
membranes,
cervical
dilation
(at
both
0
cm
10
cm),
progression
slope.
Models
incorporating
high-impact
demonstrated
superior
compared
those
utilizing
only
pregnancy-related
data.
The
Random
Forest
achieved
an
accuracy
94%
AUC
0.96
predicting
SHAP
further
identified
key
predictors
across
different
stages
including
(body
mass
index,
prior
vaginal
birth,
age),
static
(spontaneous
time
since
rupture),
dynamic
(cervical
slope).
Conclusion:
integrative
approach,
which
combines
expertise
with
analytics,
provides
clinicians
valuable
tool
for
evaluation
decision-making.
By
offering
more
accurate
predictions
progression,
particularly
context
VBAC,
this
approach
significantly
improve
neonatal
outcomes
Applied Sciences,
Год журнала:
2025,
Номер
15(6), С. 3355 - 3355
Опубликована: Март 19, 2025
This
study
examines
the
security
implications
of
generative
artificial
intelligence
(GAI),
focusing
on
models
such
as
ChatGPT.
As
GAI
technologies
are
increasingly
integrated
into
industries
like
healthcare,
education,
and
media,
concerns
growing
regarding
vulnerabilities,
ethical
challenges,
potential
for
misuse.
not
only
synthesizes
existing
research
but
also
conducts
an
original
scientometric
analysis
using
text
mining
techniques.
To
address
these
concerns,
this
analyzes
1047
peer-reviewed
academic
articles
from
SCOPUS
database
methods,
including
Term
Frequency–Inverse
Document
Frequency
(TF-IDF)
analysis,
keyword
centrality
Latent
Dirichlet
Allocation
(LDA)
topic
modeling.
The
results
highlight
significant
contributions
countries
United
States,
China,
India,
with
leading
institutions
Chinese
Academy
Sciences
National
University
Singapore
driving
security.
In
“ChatGPT”
emerged
a
highly
central
term,
reflecting
its
prominence
in
discourse.
However,
despite
frequent
mention,
showed
lower
proximity
than
terms
“model”
“AI”.
suggests
that
while
ChatGPT
is
broadly
associated
other
key
themes,
it
has
less
direct
connection
to
specific
subfields.
Topic
modeling
identified
six
major
AI
language
models,
data
processing,
risk
management.
emphasizes
need
robust
frameworks
technical
ensure
responsibility,
manage
risks
safe
deployment
systems.
These
must
incorporate
solutions
accountability,
regulatory
compliance,
continuous
underscores
importance
interdisciplinary
integrates
technical,
legal,
perspectives
responsible
secure
technologies.