Advances in environmental engineering and green technologies book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 353 - 380
Published: June 28, 2024
Food
security
and
maximum
yield
depend
on
accurate
pest
prediction
crop
management.
An
in-depth
analysis
of
this
cutting-edge
area
is
the
goal
book
chapter,
which
will
explore
predictive
modeling
using
machine
learning
(ML)
algorithms.
The
introduction
establishes
section
by
stressing
significance
ML
in
transforming
management
value
modeling.
Furthermore,
it
delve
into
various
techniques
designed
for
Differentiating
between
supervised,
unsupervised,
semi-supervised
techniques,
outline
a
range
methods.
Moreover,
to
help
practitioners
make
an
educated
decision,
also
focus
criteria
algorithm
selection
prediction.
It
concludes
with
detailed
overview
algorithms'
revolutionary
potential
agricultural
operations
their
importance
Clinical Neurology and Neurosurgery,
Journal Year:
2025,
Volume and Issue:
unknown, P. 108761 - 108761
Published: Jan. 1, 2025
Stroke
remains
a
leading
cause
of
death
and
disability
worldwide,
with
African
populations
bearing
disproportionately
high
burden
due
to
limited
healthcare
infrastructure.
Early
prediction
intervention
are
critical
reducing
stroke
outcomes.
This
study
developed
evaluated
system
using
Gated
Recurrent
Units
(GRU),
variant
Neural
Networks
(RNN),
leveraging
the
Afrocentric
Investigative
Research
Education
Network
(SIREN)
dataset.
The
utilized
secondary
data
from
SIREN
dataset,
comprising
4236
records
29
phenotypes.
Feature
selection
reduced
these
15
optimal
phenotypes
based
on
their
significance
occurrence.
GRU
model,
designed
128
input
neurons
four
hidden
layers
(64,
32,
16,
8
neurons),
was
trained
150
epochs,
batch
size
8,
metrics
such
as
accuracy,
AUC,
time.
Comparisons
were
made
traditional
machine
learning
algorithms
(Logistic
Regression,
SVM,
KNN)
Long
Short-Term
Memory
(LSTM)
networks.
GRU-based
achieved
performance
accuracy
77.48
%,
an
AUC
0.84,
time
0.43
seconds,
outperforming
all
other
models.
Logistic
Regression
73.58
while
LSTM
reached
74.88
%
but
longer
2.23
seconds.
significantly
improved
model's
compared
demonstrated
superior
in
prediction,
offering
efficient
scalable
tool
for
healthcare.
Future
research
should
focus
integrating
unstructured
data,
validating
model
diverse
populations,
exploring
hybrid
architectures
enhance
predictive
accuracy.
Journal of Medicine Surgery and Public Health,
Journal Year:
2024,
Volume and Issue:
3, P. 100113 - 100113
Published: May 13, 2024
The
primary
objective
of
this
commentary
was
to
identify
the
strengths
and
weaknesses
AI
technologies,
uncover
opportunities
for
improvement,
recognize
potential
threats
that
could
impede
their
successful
implementation
in
nursing
care.
This
involved
constructing
a
SWOT
matrix
analyze
adoption,
identifying
internal
weaknesses,
external
threats.
analysis
revealed
several
adoption
care,
including
enhanced
data
capabilities,
improved
patient
monitoring,
increased
efficiency
routine
tasks.
However,
such
as
high
initial
costs
concerns
about
security
were
identified.
Opportunities
included
reduce
healthcare
improve
outcomes.
Nonetheless,
resistance
technological
change
ethical
dilemmas
related
decision-making
processes
recognized
barriers
adoption.
article
sheds
light
on
intricate
landscape
While
brings
forth
substantial
strengths,
it
simultaneously
poses
challenges
systems
should
confront.
To
fully
harness
AI's
potential,
organizations
thoughtfully
deliberate
identified
threats,
actively
seeking
avenues
seamless
integration.
In
concerted
effort,
industry
is
poised
unlock
transformative
capabilities
AI,
elevating
care
standards,
ultimately,
advancing
Cureus,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 18, 2025
Background:
Cardiomyopathy
is
a
broad
category
of
myocardial
conditions
that
have
substantial
effect
on
heart
function.
Improving
patient
treatment
requires
knowledge
its
epidemiology.
Objective:
The
aim
this
study
was
to
determine
the
pattern
cardiomyopathy
in
patients
presenting
tertiary
care
hospital
Peshawar,
Pakistan.
Methodology:
This
cross-sectional
conducted
at
Department
Cardiology,
Northwest
General
Hospital
&
Research
Centre,
from
December
14,
2022,
June
2023.
There
were
79
individuals
with
who
16
years
age
or
older.
Clinical
and
demographic
information,
such
as
age,
gender,
BMI,
length
illness,
family
history,
gathered.
patterns
classified
using
echocardiographic
evaluations,
IBM
SPSS
Statistics
for
Windows,
version
25
(IBM
Corp.,
Armonk,
NY)
employed
statistical
analysis.
Results:
average
participants
45.72
±
2.45
years,
40.5%
(n=32)
between
ages
51
60.
63.3%
male
(n=50)
36.7%
female
(n=29).
With
69.6%
(n=55)
30.4%
(n=24)
having
duration
symptoms
≤1
month
>1
month,
respectively.
38.0%
(n=30)
had
history
cardiomyopathy.
dilated,
hypertrophic,
peripartum
each
15.2%,
most
prevalent
forms
restrictive
(20.3%,
n=16),
ischemic
(17.7%,
n=14),
arrhythmogenic
right
ventricular
(16.5%,
n=13).
BMI
(p
=
0.000)
illness
substantially
correlated
dilated
hypertrophic
cardiomyopathies.
Older
groups,
especially
those
60,
greater
prevalence
0.000).
Dilated
significantly
influenced
by
history.
Conclusion:
research
highlights
variety
seen
facility,
being
prevalent.
need
specialized
diagnosis
strategies.
Cureus,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 28, 2025
Cardiovascular
diseases
(CVDs)
remain
the
leading
global
cause
of
mortality,
and
a
high
prevalence
cardiac
conditions,
including
premature
deaths,
have
increased
from
decades
until
today.
However,
early
detection
management
these
conditions
are
challenging,
given
their
complexity,
scale
affected
populations,
dynamic
nature
disease
process,
treatment
approach.
The
transformative
potential
is
being
brought
by
Artificial
Intelligence
(AI),
specifically
machine
learning
(ML)
deep
technologies,
to
analyze
massive
datasets,
improve
diagnostic
accuracy,
optimize
strategy.
recent
advancements
in
such
AI-based
frameworks
as
personalization
decision-making
support
systems
for
customized
medicine
automated
image
assessments
drastically
increase
precision
efficiency
healthcare
professionals.
implementing
AI
widely
clogged
with
obstacles,
regulatory,
privacy,
validation
across
populations.
Additionally,
despite
desire
incorporate
into
clinical
routines,
there
no
shortage
concern
about
interoperability
clinician
acceptance
system.
Despite
challenges,
further
research
development
essential
overcoming
hurdles.
This
review
explores
use
cardiovascular
care,
its
limitations
current
use,
future
integration
toward
better
patient
outcomes.
Clinical Neurology and Neurosurgery,
Journal Year:
2024,
Volume and Issue:
249, P. 108689 - 108689
Published: Dec. 10, 2024
Stroke
is
a
leading
cause
of
morbidity
and
mortality
worldwide,
early
detection
risk
factors
critical
for
prevention
improved
outcomes.
Traditional
stroke
assessments,
relying
on
sporadic
clinical
visits,
fail
to
capture
dynamic
changes
in
such
as
hypertension
atrial
fibrillation
(AF).
Wearable
technology
(devices),
combined
with
biometric
data
analysis,
offers
transformative
approach
by
enabling
continuous
monitoring
physiological
parameters.
This
narrative
review
was
conducted
using
systematic
identify
analyze
peer-reviewed
articles,
reports,
case
studies
from
reputable
scientific
databases.
The
search
strategy
focused
articles
published
between
2010
till
date
pre-determined
keywords.
Relevant
were
selected
based
their
focus
wearable
devices
AI-driven
technologies
prevention,
diagnosis,
rehabilitation.
literature
categorized
thematically
explore
applications,
opportunities,
challenges,
future
directions.
explores
the
current
landscape
assessment,
focusing
role
detection,
personalized
care,
integration
into
practice.
highlights
opportunities
presented
predictive
analytics,
where
algorithms
can
provide
tailored
interventions.
Personalized
powered
machine
learning,
enable
individualized
care
plans.
Furthermore,
telemedicine
facilitates
remote
patient
rehabilitation,
particularly
underserved
areas.
Despite
these
advances,
challenges
remain.
Issues
accuracy,
privacy
concerns,
wearables
healthcare
systems
must
be
addressed
fully
realize
potential.
As
evolves,
its
application
could
revolutionize
improving
outcomes
reducing
global
burden
stroke.
Diseases,
Journal Year:
2025,
Volume and Issue:
13(1), P. 24 - 24
Published: Jan. 20, 2025
Background:
Cancer
remains
a
leading
cause
of
morbidity
and
mortality
worldwide.
Traditional
treatments
like
chemotherapy
radiation
often
result
in
significant
side
effects
varied
patient
outcomes.
Immunotherapy
has
emerged
as
promising
alternative,
harnessing
the
immune
system
to
target
cancer
cells.
However,
complexity
responses
tumor
heterogeneity
challenges
its
effectiveness.
Objective:
This
mini-narrative
review
explores
role
artificial
intelligence
[AI]
enhancing
efficacy
immunotherapy,
predicting
responses,
discovering
novel
therapeutic
targets.
Methods:
A
comprehensive
literature
was
conducted,
focusing
on
studies
published
between
2010
2024
that
examined
application
AI
immunotherapy.
Databases
such
PubMed,
Google
Scholar,
Web
Science
were
utilized,
articles
selected
based
relevance
topic.
Results:
significantly
contributed
identifying
biomarkers
predict
immunotherapy
by
analyzing
genomic,
transcriptomic,
proteomic
data.
It
also
optimizes
combination
therapies
most
effective
treatment
protocols.
AI-driven
predictive
models
help
assess
response
guiding
clinical
decision-making
minimizing
effects.
Additionally,
facilitates
discovery
targets,
neoantigens,
enabling
development
personalized
immunotherapies.
Conclusions:
holds
immense
potential
transforming
related
data
privacy,
algorithm
transparency,
integration
must
be
addressed.
Overcoming
these
hurdles
will
likely
make
central
component
future
offering
more
treatments.
International Journal of Medical Informatics,
Journal Year:
2025,
Volume and Issue:
199, P. 105909 - 105909
Published: April 6, 2025
Artificial
Intelligence
(AI)
is
increasingly
being
integrated
into
healthcare
to
improve
diagnostics,
treatment
planning,
and
operational
efficiency.
However,
its
adoption
raises
significant
concerns
related
data
privacy,
ethical
integrity,
regulatory
compliance.
While
much
of
the
existing
literature
focuses
on
clinical
applications
AI,
limited
attention
has
been
given
perspectives
Information
Governance
(IG)
professionals,
who
play
a
critical
role
in
ensuring
responsible
compliant
AI
implementation
within
systems.
This
study
aims
explore
perceptions
IG
professionals
Kent,
United
Kingdom,
use
delivery
research,
with
focus
governance,
considerations,
implications.
A
qualitative
exploratory
design
was
employed.
Six
from
NHS
trusts
Kent
were
purposively
selected
based
their
roles
compliance,
policy
enforcement.
Semi-structured
interviews
conducted
thematically
analysed
using
NVivo
software,
guided
by
Unified
Theory
Acceptance
Use
Technology
(UTAUT).
Thematic
analysis
revealed
varying
levels
knowledge
among
professionals.
participants
acknowledged
AI's
potential
efficiency,
they
raised
about
accuracy,
algorithmic
bias,
cybersecurity
risks,
unclear
frameworks.
Participants
also
highlighted
importance
need
for
national
oversight.
offers
promising
opportunities
healthcare,
but
must
be
underpinned
robust
governance
structures.
Enhancing
literacy
teams
establishing
clearer
frameworks
will
key
safe
implementation.
