Review on Pharmaceutical Industries Production of Medicines for Lung Cancer Diseases Prevention and Side Effects DataSets View and Analysis with Orange Software Visualization Techniques
International Journal of Management Technology and Social Sciences,
Год журнала:
2025,
Номер
unknown, С. 17 - 44
Опубликована: Янв. 30, 2025
Purpose:
The
purpose
of
this
research
is
to
explore
how
Orange,
a
powerful
information
extraction
and
predictive
modeling
software,
can
be
applied
in
the
pharmaceutical
industry
assess
visualize
effectiveness
cancer
prevention
medicines.
By
focusing
on
companies
like
Genentech
Inc.
(USA),
AstraZeneca
Pharmaceutical
PLC
(UK),
Boehringer
Ingelheim
(Germany),
Chugai
Co.
Ltd.
(Japan),
study
seeks
evaluate
which
cancer-preventing
drugs
from
these
provide
best
efficacy
while
minimizing
side
effects
for
patients.
goal
assist
healthcare
professionals
(doctors
pharmacists)
making
informed
decisions
about
most
suitable
medications
prevention,
ensuring
patient
safety
optimal
treatment
outcomes.
Design/Methodology/Approach:
This
utilizes
Orange
software’s
machine
learning
data
visualization
tools,
specifically
scatterplot
graphs,
analyze
complex
datasets
related
drugs.
using
scatterplots
concurrently
examine
multiple
parameters,
such
as
Company
Name,
Drug
Class,
Medicine
Prevention
Cancer
Diseases,
Side
Effects
Percentage
aims
identify
patterns
correlations
that
help
drug
safety.
approach
involves
analyzing
relationship
between
characteristics
effects,
providing
actionable
insights
into
different
treatments
might
interact
with
health
conditions.
Findings/Results:
findings
suggest
Orange’s
visualizations
valuable
various
medicines
across
companies.
enabling
simultaneous
analysis
software
helps
are
effective
preventing
effects.
provides
clearer
understanding
characteristics,
outcomes,
supporting
data-driven
decision-making
development
practices.
Originality/Value:
originality
lies
application
mining
capabilities
relationships
within
datasets.
use
efficacy,
an
innovative
offers
richer,
more
nuanced
contributes
optimizing
choice
strategies,
ultimately
improving
therapeutic
Paper
Type:
analytical
paper
applies
techniques
focuses
tools
extract
interpret
data,
professionals.
Язык: Английский
Digital Biomarkers for Parkinson's Disease: A Bibliometric Analysis and a Scoping Review of Deep Learning for Freezing of Gait (Preprint)
Journal of Medical Internet Research,
Год журнала:
2025,
Номер
27, С. e71560 - e71560
Опубликована: Март 19, 2025
With
the
rapid
development
of
digital
biomarkers
in
Parkinson
disease
(PD)
research,
it
has
become
increasingly
important
to
explore
current
research
trends
and
key
areas
focus.
This
study
aimed
comprehensively
evaluate
status,
hot
spots,
future
global
PD
biomarker
provide
a
systematic
review
deep
learning
models
for
freezing
gait
(FOG)
biomarkers.
used
bibliometric
analysis
based
on
Web
Science
Core
Collection
database
conduct
comprehensive
multidimensional
landscape
After
identifying
also
followed
PRISMA-ScR
(Preferred
Reporting
Items
Systematic
Reviews
Meta-Analyses
Extension
Scoping
Reviews)
guidelines
scoping
FOG
from
5
databases:
Science,
PubMed,
IEEE
Xplore,
Embase,
Google
Scholar.
A
total
750
studies
were
included
analysis,
40
review.
The
revealed
growing
number
related
publications,
with
3700
researchers
contributing.
Neurology
had
highest
average
annual
participation
rate
(12.46/19,
66%).
United
States
contributed
most
(192/1171,
16.4%),
210
participating
institutions,
which
was
among
all
countries.
In
FOG,
accuracy
0.92,
sensitivity
0.88,
specificity
0.90,
area
under
curve
0.91.
addition,
31
(78%)
indicated
that
best
primarily
convolutional
neural
networks
or
networks-based
architectures.
Research
is
currently
at
stable
stage
development,
widespread
interest
countries,
researchers.
However,
challenges
remain,
including
insufficient
interdisciplinary
interinstitutional
collaboration,
as
well
lack
corporate
funding
projects.
Current
focus
motor-related
studies,
particularly
monitoring.
still
external
validation
standardized
performance
reporting.
Future
will
likely
progress
toward
deeper
applications
artificial
intelligence,
enhanced
different
data
types,
exploration
broader
range
symptoms.
Open
Foundation
(OSF
Registries)
OSF.IO/RG8Y3;
https://doi.org/10.17605/OSF.IO/RG8Y3.
Язык: Английский
Digital Biomarkers for Parkinson Disease: Bibliometric Analysis and a Scoping Review of Deep Learning for Freezing of Gait (Preprint)
Опубликована: Янв. 21, 2025
BACKGROUND
With
the
rapid
development
of
digital
biomarkers
in
Parkinson
disease
(PD)
research,
it
has
become
increasingly
important
to
explore
current
research
trends
and
key
areas
focus.
OBJECTIVE
This
study
aimed
comprehensively
evaluate
status,
hot
spots,
future
global
PD
biomarker
provide
a
systematic
review
deep
learning
models
for
freezing
gait
(FOG)
biomarkers.
METHODS
used
bibliometric
analysis
based
on
Web
Science
Core
Collection
database
conduct
comprehensive
multidimensional
landscape
After
identifying
also
followed
PRISMA-ScR
(Preferred
Reporting
Items
Systematic
Reviews
Meta-Analyses
Extension
Scoping
Reviews)
guidelines
scoping
FOG
from
5
databases:
Science,
PubMed,
IEEE
Xplore,
Embase,
Google
Scholar.
RESULTS
A
total
750
studies
were
included
analysis,
40
review.
The
revealed
growing
number
related
publications,
with
3700
researchers
contributing.
Neurology
had
highest
average
annual
participation
rate
(12.46/19,
66%).
United
States
contributed
most
(192/1171,
16.4%),
210
participating
institutions,
which
was
among
all
countries.
In
FOG,
accuracy
0.92,
sensitivity
0.88,
specificity
0.90,
area
under
curve
0.91.
addition,
31
(78%)
indicated
that
best
primarily
convolutional
neural
networks
or
networks–based
architectures.
CONCLUSIONS
Research
is
currently
at
stable
stage
development,
widespread
interest
countries,
researchers.
However,
challenges
remain,
including
insufficient
interdisciplinary
interinstitutional
collaboration,
as
well
lack
corporate
funding
projects.
Current
focus
motor-related
studies,
particularly
monitoring.
still
external
validation
standardized
performance
reporting.
Future
will
likely
progress
toward
deeper
applications
artificial
intelligence,
enhanced
different
data
types,
exploration
broader
range
symptoms.
CLINICALTRIAL
Open
Foundation
(OSF
Registries)
OSF.IO/RG8Y3;
https://doi.org/10.17605/OSF.IO/RG8Y3
Язык: Английский
Mapping knowledge landscapes and emerging trends of artificial intelligence in the early screening of cognitive impairment diseases
Xin Liu,
Sixie Li,
Shuying Shen
и другие.
Опубликована: Март 28, 2025
Язык: Английский
Detection of Alzheimer's Disease in Neuroimages using Vision Transformers: A Systematic Review and Meta-Analysis (Preprint)
Journal of Medical Internet Research,
Год журнала:
2024,
Номер
27, С. e62647 - e62647
Опубликована: Ноя. 30, 2024
Background
Alzheimer
disease
(AD)
is
a
progressive
condition
characterized
by
cognitive
decline
and
memory
loss.
Vision
transformers
(ViTs)
are
emerging
as
promising
deep
learning
models
in
medical
imaging,
with
potential
applications
the
detection
diagnosis
of
AD.
Objective
This
review
systematically
examines
recent
studies
on
application
ViTs
detecting
AD,
evaluating
diagnostic
accuracy
impact
network
architecture
model
performance.
Methods
We
conducted
systematic
search
across
major
databases,
including
China
National
Knowledge
Infrastructure,
CENTRAL
(Cochrane
Central
Register
Controlled
Trials),
ScienceDirect,
PubMed,
Web
Science,
Scopus,
covering
publications
from
January
1,
2020,
to
March
2024.
A
manual
was
also
performed
include
relevant
gray
literature.
The
included
papers
used
ViT
for
AD
versus
healthy
controls
based
neuroimaging
data,
magnetic
resonance
imaging
positron
emission
tomography.
Pooled
estimates,
sensitivity,
specificity,
likelihood
ratios,
odds
were
derived
using
random-effects
models.
Subgroup
analyses
comparing
performance
different
architectures
performed.
Results
meta-analysis,
encompassing
11
95%
CIs
P
values,
demonstrated
pooled
accuracy:
sensitivity
0.925
(95%
CI
0.892-0.959;
P<.01),
specificity
0.957
0.932-0.981;
positive
ratio
21.84
12.26-38.91;
negative
0.08
0.05-0.14;
P<.01).
area
under
curve
notably
high
at
0.924.
findings
highlight
effective
tools
early
accurate
diagnosis,
offering
insights
future
neuroimaging-based
approaches.
Conclusions
provides
valuable
evidence
utility
distinguishing
patients
controls,
thereby
contributing
advancements
methodologies.
Trial
Registration
PROSPERO
CRD42024584347;
https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=584347
Язык: Английский