Frontiers in Nutrition,
Год журнала:
2024,
Номер
11
Опубликована: Ноя. 6, 2024
Sarcopenia
is
a
loss
of
muscle
strength,
mass,
and
function
that
can
increase
patient’s
risk
injury,
illness,
even
severely
impair
quality
life
death.
A
growing
body
research
suggests
sarcopenia
urinary
tract
disorders
are
closely
related.
In
this
review,
we
aimed
to
emphasize
the
definition
skeletal
sarcopenia,
summarize
methods
used
diagnose
discuss
advances
in
study
benign
diseases
system,
malignant
system.
urologic
interact
with
each
other;
cause
aggravates
condition
original
disease,
thus
falling
into
vicious
circle.
This
review
provides
comprehensive
understanding
diseases,
which
very
important
for
management
prognosis
diseases.
Journal of Cachexia Sarcopenia and Muscle,
Год журнала:
2023,
Номер
14(5), С. 2044 - 2053
Опубликована: Июль 12, 2023
Skeletal
muscle
loss
during
treatment
is
associated
with
poor
survival
outcomes
in
patients
ovarian
cancer.
Although
changes
mass
can
be
assessed
on
computed
tomography
(CT)
scans,
this
labour-intensive
process
impair
its
utility
clinical
practice.
This
study
aimed
to
develop
a
machine
learning
(ML)
model
predict
based
data
and
interpret
the
ML
by
applying
SHapley
Additive
exPlanations
(SHAP)
method.
International Journal of Environmental Health Research,
Год журнала:
2025,
Номер
unknown, С. 1 - 12
Опубликована: Фев. 15, 2025
With
the
intensification
of
urbanization,
air
pollution
has
garnered
global
concern.
This
study
aims
to
predict
incremental
lifetime
cancer
risk
(ILCR)
polycyclic
aromatic
hydrocarbons
(PAHs)
in
atmospheric
PM2.5.
Utilizing
machine
learning
regression
algorithms
and
data
from
six
cities
Jiangsu
Province
2018,
we
established
models
investigate
relationship
between
ILCR
various
factors,
with
a
special
emphasis
on
seasonal
meteorological
data.
After
model
training,
SHapley
Additive
exPlanation
(SHAP)
analysis
revealed
that
factors
were
even
more
influential
than
PM2.5
predicting
ILCR.
Models
then
validated
using
2019
data,
resulting
an
R2
0.42,
which
indicated
decrease
accuracy
compared
2018
test
set
0.74
but
still
represented
improvement
over
alone
(R2
=
0.2).
suggests
while
related
are
crucial,
additional
needed
build
robust
for
future
predictions.
Aging Clinical and Experimental Research,
Год журнала:
2025,
Номер
37(1)
Опубликована: Март 1, 2025
Abstract
Objectives
Sarcopenic
obesity
(SO),
characterized
by
the
coexistence
of
and
sarcopenia,
is
an
increasingly
prevalent
condition
in
aging
populations,
associated
with
numerous
adverse
health
outcomes.
We
aimed
to
identify
validate
explainable
prediction
model
SO
using
easily
available
clinical
characteristics.
Setting
participants
A
preliminary
cohort
1,431
from
three
community
regions
Ziyang
city,
China,
was
used
for
development
internal
validation.
For
external
validation,
we
utilized
data
832
residents
multi-center
nursing
homes.
Measurements
The
diagnosis
based
on
European
Society
Clinical
Nutrition
Metabolism
(ESPEN)
Association
Study
Obesity
(EASO)
criteria.
Five
machine
learning
models
(support
vector
machine,
logistic
regression,
random
forest,
light
gradient
boosting
extreme
boosting)
were
predict
SO.
performance
these
assessed
area
under
receiver
operating
characteristic
curve
(AUC).
SHapley
Additive
exPlanations
(SHAP)
approach
interpretation.
Results
After
feature
reduction,
8-feature
demonstrated
good
predictive
ability.
Among
five
tested,
support
(SVM)
performed
best
both
(AUC
=
0.862)
0.785)
validation
sets.
eight
key
predictors
identified
BMI,
gender,
neck
circumference,
waist
thigh
time
full
tandem
standing,
five-times
sit-to-stand,
age.
SHAP
analysis
revealed
BMI
gender
as
most
influential
predictors.
To
facilitate
utilization
SVM
setting,
developed
a
web
application
(
https://svcpredictapp.streamlit.app/
).
Conclusions
populations.
This
offers
novel,
accessible,
interpretable
potential
enhance
early
detection
intervention
strategies.
Further
studies
are
warranted
our
diverse
populations
evaluate
its
impact
patient
outcomes
when
integrated
into
comprehensive
geriatric
assessments.
Frontiers in Medicine,
Год журнала:
2025,
Номер
12
Опубликована: Март 13, 2025
Introduction
Long
COVID
significantly
affects
patients'
quality
of
life,
yet
no
standardized
treatment
has
been
established.
Traditional
Chinese
Medicine
(TCM)
presents
a
promising
potential
approach
with
targeted
therapeutic
strategies.
This
study
aims
to
develop
an
explainable
machine
learning
(ML)
model
and
nomogram
identify
patients
who
may
benefit
from
TCM,
enhancing
clinical
decision-making.
Methods
We
analyzed
data
1,331
treated
TCM
between
December
2022
February
2024
at
three
hospitals
in
Zhejiang,
China.
Effectiveness
was
defined
as
improvement
two
or
more
symptoms
minimum
2-point
increase
the
Syndrome
Score
(TCMSS).
Data
included
11
patient
disease
characteristics,
18
syndrome
scores,
12
auxiliary
examination
indicators.
The
least
absolute
shrinkage
selection
operator
(LASSO)
method
identified
features
linked
efficacy.
1,204
served
training
set,
while
127
formed
testing
set.
Results
employed
five
ML
algorithms:
Support
Vector
Machine
(SVM),
Random
Forest
(RF),
K-Nearest
Neighbors
(KNN),
Extreme
Gradient
Boosting
(XGBoost),
Neural
Network
(NN).
XGBoost
achieved
Area
Under
Curve
(AUC)
0.9957
F1
score
0.9852
demonstrating
superior
performance
set
AUC
0.9059
0.9027.
Key
through
SHapley
Additive
exPlanations
(SHAP)
chest
tightness,
aversion
cold,
age,
TCMSS,
Short
Form
(36)
Health
Survey
(SF-36),
C-reactive
protein
(CRP),
lymphocyte
ratio.
logistic
regression-based
demonstrated
0.9479
0.9384
Conclusion
utilized
multicenter
multiple
algorithms
create
for
predicting
efficacy
treatment.
Furthermore,
developed
assist
improve
decision-making
efficiency
applications
management.
