Early and Rapid COVID-19 Diagnosis Using a Symptom-Based Machine Learning Model
International Journal of Innovative Science and Research Technology (IJISRT),
Год журнала:
2024,
Номер
unknown, С. 1537 - 1543
Опубликована: Авг. 2, 2024
The
COVID-19
pandemic
has
resulted
in
a
significant
global
health
crisis,
claiming
over
6.3
million
lives.
Rapid
and
accurate
detection
of
symptoms
is
essential
for
effective
public
responses.
This
study
utilizes
machine
learning
algorithms
to
enhance
the
speed
accuracy
diagnosis
based
on
symptom
data.
By
employing
Spearman
feature
selection
algorithm,
we
identified
most
predictive
features,
thereby
improving
model
performance
reducing
number
features
required.
decision
tree
algorithm
proved
be
effective,
achieving
an
98.57%,
perfect
sensitivity
1,
high
specificity
0.97.
Our
results
indicate
that
combining
various
with
AI-based
techniques
can
accurately
detect
patients.
These
findings
surpass
previous
studies,
demonstrating
superior
across
multiple
evaluations.
integration
advanced
models
offers
practical
efficient
tool
early
diagnosis,
patient
management
approach
holds
promise
enhancing
healthcare
delivery.
Язык: Английский
Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data
Nuklearmedizin - NuclearMedicine,
Год журнала:
2023,
Номер
62(06), С. 389 - 398
Опубликована: Окт. 31, 2023
Abstract
Nuclear
imaging
techniques
such
as
positron
emission
tomography
(PET)
and
single
photon
computed
(SPECT)
in
combination
with
(CT)
are
established
modalities
clinical
practice,
particularly
for
oncological
problems.
Due
to
a
multitude
of
manufacturers,
different
measurement
protocols,
local
demographic
or
workflow
variations
well
various
available
reconstruction
analysis
software,
very
heterogeneous
datasets
generated.
This
review
article
examines
the
current
state
interoperability
harmonisation
image
data
related
field
nuclear
medicine.
Various
approaches
standards
improve
compatibility
integration
discussed.
These
include,
example,
structured
history,
standardisation
acquisition
standardised
preparation
evaluation.
Approaches
acquisition,
storage
will
be
presented.
Furthermore,
presented
prepare
way
that
they
become
usable
projects
applying
artificial
intelligence
(AI)
(machine
learning,
deep
etc.).
concludes
an
outlook
on
future
developments
trends
AI
medicine,
including
brief
research
commercial
solutions.
Язык: Английский
Overview of medical analysis capabilities in radiology of current Artificial Intelligence models
Quality in Sport,
Год журнала:
2024,
Номер
20, С. 53933 - 53933
Опубликована: Авг. 19, 2024
Judgment
is
fundamental
in
medicine,
particularly
when
combining
complex
data
layers
with
detailed
decision-making
processes.
Radiology
processes
present
a
distinct
challenge
for
medical
decisions
due
to
the
amount
and
shortage
time
personnel
capable
of
analyzing
images.
Additionally,
it's
crucial
consider
each
patient's
specific
situation,
including
their
current
state
disease
history.
Despite
advancements
technology,
there
are
still
significant
hurdles
accurately
radiology
data.
Radiographic
assessments,
which
predominantly
based
on
visual
inspections,
could
greatly
benefit
from
enhanced
computational
analyses.
Artificial
intelligence
(AI)
particular
holds
potential
significantly
improve
qualitative
interpretation
imaging
by
experts
-
automating
even
replacing
some
parts
work.
This
article
will
be
an
overview
possibilities
challenges
associated
introducing
new
technology
into
spaces.
Doctors
struggling
it
limits
how
much
care
they
can
show
patient.
The
image
marked
most
important
parts,
AI
produce
more
user
friendly
version
description,
suggesting
what
might
problem
later
human
evaluation.
Understanding
or
cutting
down
spend
analyze
allow
faster
deliver
radiologic
description
doctors
dealing
patient
treatment.
Язык: Английский
Development of a novel machine learning model based on laboratory and imaging indices to predict acute cardiac injury in cancer patients with COVID-19 infection: a retrospective observational study
Journal of Cancer Research and Clinical Oncology,
Год журнала:
2023,
Номер
149(19), С. 17039 - 17050
Опубликована: Сен. 25, 2023
Язык: Английский
Challenges in Implementing the Local Node Infrastructure for a National Federated Machine Learning Network in Radiology
Healthcare,
Год журнала:
2023,
Номер
11(17), С. 2377 - 2377
Опубликована: Авг. 23, 2023
Data-driven
machine
learning
in
medical
research
and
diagnostics
needs
large-scale
datasets
curated
by
clinical
experts.
The
generation
of
large
can
be
challenging
terms
resource
consumption
time
effort,
while
generalizability
validation
the
developed
models
significantly
benefit
from
variety
data
sources.
Training
algorithms
on
smaller
decentralized
through
federated
reduce
but
require
implementation
a
specific
ambitious
infrastructure
to
share
data,
computing
time.
Additionally,
it
offers
opportunity
maintaining
keeping
locally.
Thus,
safety
issues
avoided
because
patient
must
not
shared.
Machine
are
trained
local
sharing
model
an
established
network.
In
addition
commercial
applications,
there
also
numerous
academic
customized
implementations
network
infrastructures
available.
configuration
these
networks
primarily
differs,
yet
adheres
standard
framework
composed
fundamental
components.
this
technical
note,
we
propose
basic
requirements
for
governance,
science
workflows,
node
set-up,
report
advantages
experienced
pitfalls
implementing
with
German
Radiological
Cooperative
Network
initiative
as
use
case
example.
We
show
how
built
upon
some
base
components
reflect
they
implemented
considering
both
global
requirements.
After
analyzing
deployment
process
different
settings
scenarios,
recommend
integrating
into
existing
IT
infrastructure.
This
approach
benefits
maintenance
effort
compared
external
integration
separate
environment
(e.g.,
radiology
department).
proposed
groundwork
taken
exemplary
development
guideline
future
applications
scientific
environments.
Язык: Английский