IEEE Access,
Год журнала:
2022,
Номер
10, С. 95106 - 95124
Опубликована: Янв. 1, 2022
The
novel
coronavirus
(nCOV)
is
a
new
strain
that
needs
to
be
hindered
from
spreading
by
taking
effective
preventive
measures
as
swiftly
possible.
Timely
forecasting
of
COVID-19
cases
can
ultimately
support
in
making
significant
decisions
and
planning
for
implementing
measures.
In
this
study,
three
common
machine
learning
(ML)
approaches
via
linear
regression
(LR),
sequential
minimal
optimization
(SMO)
regression,
M5P
techniques
have
been
discussed
implemented
disease-2019
(COVID-19)
pandemic
scenarios.
To
demonstrate
the
forecast
accuracy
aforementioned
ML
approaches,
preliminary
sample-study
has
conducted
on
first
wave
scenario
different
countries
including
United
States
America
(USA),
Italy,
Australia.
Furthermore,
contributions
study
are
extended
conducting
an
in-depth
scenarios
first,
second,
third
waves
India.
An
accurate
model
proposed,
which
constructed
basis
results
models
findings
research
highlight
LR
potential
approach
outperforms
all
other
tested
herein
present
scenario.
Finally,
used
likely
onset
fourth
Journal of Science and Technology Policy Management,
Год журнала:
2022,
Номер
15(3), С. 506 - 529
Опубликована: Фев. 14, 2022
Purpose
The
purpose
of
this
paper
is
to
give
an
overview
artificial
intelligence
(AI)
and
other
AI-enabled
technologies
describe
how
COVID-19
affects
various
industries
such
as
health
care,
manufacturing,
retail,
food
services,
education,
media
entertainment,
banking
insurance,
travel
tourism.
Furthermore,
the
authors
discuss
tactics
in
which
information
technology
used
implement
business
strategies
transform
businesses
incentivise
implementation
these
current
or
future
emergency
situations.
Design/methodology/approach
review
provides
rapidly
growing
literature
on
use
smart
during
pandemic.
Findings
127
empirical
articles
have
identified
suggest
that
39
forms
been
used,
ranging
from
computer
vision
technology.
Eight
different
are
using
technologies,
primarily
services
manufacturing.
Further,
list
40
generalised
types
activities
involved
including
providing
data
analysis
communication.
To
prevent
spread
illness,
robots
with
being
examine
patients
drugs
them.
online
execution
teaching
practices
simulators
replaced
classroom
mode
due
epidemic.
AI-based
Blue-dot
algorithm
aids
detection
early
warning
indications.
AI
model
detects
a
patient
respiratory
distress
based
face
detection,
recognition,
facial
action
unit
expression
posture,
extremity
movement
analysis,
visitation
frequency
sound
pressure
light
level
detection.
above
applications
listed
throughout
paper.
Research
limitations/implications
largely
delimited
area
COVID-19-related
studies.
Also,
bias
selective
assessment
may
be
present.
In
Indian
context,
advanced
yet
harnessed
its
full
extent.
educational
system
upgraded
add
potential
benefits
wider
basis.
Practical
implications
First,
leveraging
insights
across
industry
sectors
battle
global
threat,
one
key
takeaways
field.
Second,
integrated
framework
recommended
for
policy
making
area.
Lastly,
recommend
internet-based
repository
should
developed,
keeping
all
ideas,
databases,
best
practices,
dashboard
real-time
statistical
data.
Originality/value
As
relatively
recent
phenomenon,
comprehensive
does
not
exist
extant
authors’
knowledge.
emerging
International Journal of Medical Informatics,
Год журнала:
2022,
Номер
166, С. 104855 - 104855
Опубликована: Авг. 17, 2022
Artificial
intelligence
is
fueling
a
new
revolution
in
medicine
and
the
healthcare
sector.
Despite
growing
evidence
on
benefits
of
artificial
there
are
several
aspects
that
limit
measure
its
impact
people's
health.
It
necessary
to
assess
current
status
application
AI
towards
improvement
health
domains
defined
by
WHO's
Thirteenth
General
Programme
Work
(GPW13)
European
(EPW),
inform
about
trends,
gaps,
opportunities,
challenges.
Osong Public Health and Research Perspectives,
Год журнала:
2024,
Номер
15(2), С. 115 - 136
Опубликована: Март 28, 2024
Objectives:
The
coronavirus
disease
2019
(COVID-19)
pandemic
continues
to
pose
significant
challenges
the
public
health
sector,
including
that
of
United
Arab
Emirates
(UAE).
objective
this
study
was
assess
efficiency
and
accuracy
various
deep-learning
models
in
forecasting
COVID-19
cases
within
UAE,
thereby
aiding
nation’s
authorities
informed
decision-making.Methods:
This
utilized
a
comprehensive
dataset
encompassing
confirmed
cases,
demographic
statistics,
socioeconomic
indicators.
Several
advanced
deep
learning
models,
long
short-term
memory
(LSTM),
bidirectional
LSTM,
convolutional
neural
network
(CNN),
CNN-LSTM,
multilayer
perceptron,
recurrent
(RNN)
were
trained
evaluated.
Bayesian
optimization
also
implemented
fine-tune
these
models.Results:
evaluation
framework
revealed
each
model
exhibited
different
levels
predictive
precision.
Specifically,
RNN
outperformed
other
architectures
even
without
optimization.
Comprehensive
perspective
analytics
conducted
scrutinize
dataset.Conclusion:
transcends
academic
boundaries
by
offering
critical
insights
enable
UAE
deploy
targeted
data-driven
interventions.
model,
which
identified
as
most
reliable
accurate
for
specific
context,
can
significantly
influence
decisions.
Moreover,
broader
implications
research
validate
capability
techniques
handling
complex
datasets,
thus
transformative
potential
healthcare
sectors.
Computers in Biology and Medicine,
Год журнала:
2021,
Номер
138, С. 104868 - 104868
Опубликована: Сен. 13, 2021
COVID-19
is
one
of
the
biggest
challenges
that
human
beings
have
faced
recently.
Many
researchers
proposed
different
prediction
methods
for
establishing
a
virus
transmission
model
and
predicting
trend
COVID-19.
Among
them,
based
on
artificial
intelligence
are
currently
most
interesting
widely
used.
However,
only
using
cannot
capture
time
change
pattern
infectious
diseases.
To
solve
this
problem,
paper
proposes
time-dependent
SIRVD
by
deep
learning.
This
combines
learning
technology
with
mathematical
diseases,
forecasts
parameters
in
diseases
fusing
models
such
as
LSTM
other
methods.
In
current
situation
mass
vaccination,
we
analyzed
data
from
January
15,
2021,
to
May
27,
2021
seven
countries
-
India,
Argentina,
Brazil,
South
Korea,
Russia,
United
Kingdom,
France,
Germany,
Italy.
The
experimental
results
show
not
has
50%
improvement
single-day
predictions
compared
pure
methods,
but
also
can
be
adapted
short-
medium-term
predictions,
which
makes
overall
more
interpretable
robust.
JMIR AI,
Год журнала:
2023,
Номер
2, С. e42936 - e42936
Опубликована: Фев. 7, 2023
Background
Emerging
artificial
intelligence
(AI)
applications
have
the
potential
to
improve
health,
but
they
may
also
perpetuate
or
exacerbate
inequities.
Objective
This
review
aims
provide
a
comprehensive
overview
of
health
equity
issues
related
use
AI
and
identify
strategies
proposed
address
them.
Methods
We
searched
PubMed,
Web
Science,
IEEE
(Institute
Electrical
Electronics
Engineers)
Xplore
Digital
Library,
ProQuest
U.S.
Newsstream,
Academic
Search
Complete,
Food
Drug
Administration
(FDA)
website,
ClinicalTrials.gov
academic
gray
literature
that
were
published
between
2014
2021
additional
during
COVID-19
pandemic
from
2020
2021.
Literature
was
eligible
for
inclusion
in
our
if
it
identified
at
least
one
issue
corresponding
strategy
it.
To
organize
synthesize
issues,
we
adopted
4-step
application
framework:
Context,
Data
Characteristics,
Model
Design,
Deployment.
then
created
many-to-many
mapping
links
strategies.
Results
In
660
documents,
18
15
Equity
Characteristics
Design
most
common.
The
common
recommended
improving
quantity
quality
data,
evaluating
disparities
introduced
by
an
application,
increasing
model
reporting
transparency,
involving
broader
community
development,
governance.
Conclusions
Stakeholders
should
when
planning,
developing,
implementing
care
so
can
make
appropriate
plans
ensure
populations
affected
their
products.
developers
consider
adopting
equity-focused
checklists,
regulators
such
as
FDA
requiring
Given
limited
documents
online,
unpublished
knowledge
unable
identify.
Expert Review of Pharmacoeconomics & Outcomes Research,
Год журнала:
2023,
Номер
24(1), С. 63 - 115
Опубликована: Ноя. 13, 2023
The
increasing
availability
of
data
and
computing
power
has
made
machine
learning
(ML)
a
viable
approach
to
faster,
more
efficient
healthcare
delivery.
Life,
Год журнала:
2021,
Номер
11(11), С. 1118 - 1118
Опубликована: Окт. 21, 2021
Accurate
prediction
models
have
become
the
first
goal
for
aiding
pandemic-related
decisions.
Modeling
and
predicting
number
of
new
active
cases
deaths
are
important
steps
anticipating
controlling
COVID-19
outbreaks.
The
aim
this
research
was
to
develop
an
accurate
system
pandemic
that
can
predict
numbers
in
Gulf
countries
Saudi
Arabia,
Oman,
United
Arab
Emirates
(UAE),
Kuwait,
Bahrain,
Qatar.
novelty
proposed
approach
is
it
uses
advanced
model-the
bidirectional
long
short-term
memory
(Bi-LSTM)
network
deep
learning
model.
datasets
were
collected
from
available
repository
containing
updated
registered
showing
global
deaths.
Statistical
analyses
(e.g.,
mean
square
error,
root
absolute
Spearman's
correlation
coefficient)
employed
evaluate
results
adopted
Bi-LSTM
based
on
metric
gave
predicted
confirmed
99.67%,
99.34%,
99.94%,
99.64%,
98.95%,
99.91%
UAE,
Qatar,
respectively,
while
testing
model
mortality
accuracies
99.87%,
97.09%,
99.53%,
98.71%,
95.62%,
99%,
respectively.
showed
significant
using
metric.
Overall,
demonstrated
success
COVID-19.
Bi-LSTM-based
achieves
optimal
effective
robust
studied
countries.
Medicina,
Год журнала:
2022,
Номер
58(4), С. 504 - 504
Опубликована: Март 31, 2022
Nowadays,
Artificial
Intelligence
(AI)
and
its
subfields,
Machine
Learning
(ML)
Deep
(DL),
are
used
for
a
variety
of
medical
applications.
It
can
help
clinicians
track
the
patient’s
illness
cycle,
assist
with
diagnosis,
offer
appropriate
therapy
alternatives.
Each
approach
employed
may
address
one
or
more
AI
problems,
such
as
segmentation,
prediction,
recognition,
classification,
regression.
However,
amount
AI-featured
research
on
Inherited
Retinal
Diseases
(IRDs)
is
currently
limited.
Thus,
this
study
aims
to
examine
artificial
intelligence
approaches
in
managing
Disorders,
from
diagnosis
treatment.
A
total
20,906
articles
were
identified
using
Natural
Language
Processing
(NLP)
method
IEEE
Xplore,
Springer,
Elsevier,
MDPI,
PubMed
databases,
papers
submitted
2010
30
October
2021
included
systematic
review.
The
resultant
demonstrates
utilized
images
different
IRD
patient
categories
most
architectures
models
their
imaging
modalities,
identifying
main
benefits
challenges
methods.
