Salud Ciencia y Tecnología - Serie de Conferencias,
Год журнала:
2022,
Номер
1, С. 91 - 91
Опубликована: Сен. 28, 2022
Al
ser
Estudiantes
Universitarios
de
zonas
vulnerables
a
nivel
socioeconómico,
la
sintomatología
depresiva
tiende
aumentarse
durante
pandemia,
por
lo
que
el
objetivo
investigación
es
determinar
depresión
en
post
COVID-19
Lima
Norte.
Es
un
estudio
cuantitativo,
descriptivo,
transversal
y
no
experimental,
con
una
población
30
pobladores
resolvieron
cuestionario
aspectos
sociodemográficos
Escala
Autoevaluación
para
Depresión
Zung.
En
sus
resultados,
5
%(n=7)
los
están
deprimidos,
30,5
%(n=10)
ligeramente
deprimidos
64,5
%(n=13)
tienen
normal.
conclusión,
esta
permitirá
destacar
las
condiciones
desfavorables
preexisten
nuestro
país,
además
producto
pandemia
se
agravó
dando
así
necesidad
hacer
intervenciones
largo
plazo
sobre
salud
mental.
The
world
health
organisation
(WHO)
revealed
approximately
280
million
people
in
the
suffer
from
depression.
Yet,
existing
studies
on
early-stage
depression
detection
using
machine
learning
(ML)
techniques
are
limited.
Prior
have
applied
a
single
stand-alone
algorithm
which
unable
to
deal
with
data
complexities,
prone
overfitting
and
limited
generalisation.
To
this
end,
our
paper
examined
performance
of
several
ML
algorithms
for
two
benchmark
social
media
datasets
(D1
D2).
More
specifically,
we
incorporated
sentiment
indicator
improve
model
performance.
Our
experimental
results
showed
that
sentence
bidirectional
encoder
representations
transformers
(SBERT)
numerical
vectors
fitted
into
stacking
ensemble
achieved
comparable
F1
scores
69%
dataset
(D1)
76%
(D2).
findings
suggest
utilising
indicators
as
additional
feature
yields
an
improved
thus,
recommend
development
depressive
term
corpus
future
work.
Diagnostics,
Год журнала:
2024,
Номер
14(21), С. 2385 - 2385
Опубликована: Окт. 25, 2024
Depression
is
a
pervasive
mental
health
condition,
particularly
affecting
older
adults,
where
early
detection
and
intervention
are
essential
to
mitigate
its
impact.
This
study
presents
an
explainable
multi-layer
dynamic
ensemble
framework
designed
detect
depression
assess
severity,
aiming
improve
diagnostic
precision
provide
insights
into
contributing
factors.
Advances in medical diagnosis, treatment, and care (AMDTC) book series,
Год журнала:
2024,
Номер
unknown, С. 97 - 119
Опубликована: Янв. 5, 2024
The
COVID-19
pandemic,
starting
in
Wuhan,
China
December
2019,
led
to
widespread
health
and
economic
challenges,
causing
millions
of
deaths
globally.
Beyond
physical
health,
it
triggered
a
mental
crisis,
especially
during
lockdowns.
To
understand
address
this,
study
collected
data
using
90
features
the
lockdown
period.
Machine
learning
(ML)
was
employed
detect
key
impacting
crises.
Three
ML
algorithms—random
forest,
random
tree,
multilayer
perceptron—were
chosen.
Random
known
for
robustness,
achieved
97.58%
accuracy.
supervised
algorithm
with
decision
trees,
yielded
93.24%
Multilayer
perceptron
(MLP),
an
artificial
neural
network,
94.20%
accuracy
by
nonlinear
relationships.
A
10-fold
cross-validation
method
used
evaluate
these
models,
enhancing
performance
reducing
bias
overfitting.
It
involves
dividing
into
ten
subsets,
training
on
nine,
evaluating
remaining,
repeating
this
times
estimate
true
unseen
data.
Big Data and Cognitive Computing,
Год журнала:
2024,
Номер
8(9), С. 112 - 112
Опубликована: Сен. 5, 2024
The
World
Health
Organisation
(WHO)
revealed
approximately
280
million
people
in
the
world
suffer
from
depression.
Yet,
existing
studies
on
early-stage
depression
detection
using
machine
learning
(ML)
techniques
are
limited.
Prior
have
applied
a
single
stand-alone
algorithm,
which
is
unable
to
deal
with
data
complexities,
prone
overfitting,
and
limited
generalization.
To
this
end,
our
paper
examined
performance
of
several
ML
algorithms
for
two
benchmark
social
media
datasets
(D1
D2).
More
specifically,
we
incorporated
sentiment
indicators
improve
model
performance.
Our
experimental
results
showed
that
sentence
bidirectional
encoder
representations
transformers
(SBERT)
numerical
vectors
fitted
into
stacking
ensemble
achieved
comparable
F1
scores
69%
dataset
(D1)
76%
(D2).
findings
suggest
utilizing
as
an
additional
feature
yields
improved
performance,
thus,
recommend
development
depressive
term
corpus
future
work.
Depression
is
a
prevalent
global
health
issue,
impacting
various
aspects
of
individuals'
lives,
including
home
and
social
interactions.
In
the
Arabic
environment,
stigma
surrounding
mental
disorders
limited
awareness
in
psychiatry
domain
has
made
early
diagnosis
depression
challenging
task.
However,
media
platforms
have
enabled
individuals
to
express
their
thoughts
personal
experiences,
making
these
valuable
resource
for
monitoring.
this
paper,
we
propose
an
approach
predict
signs
utilizing
posts
expressed
on
Twitter
platform.
The
proposed
methodology
integrates
knowledge
extracted
using
LLM-based
transformer,
UMLS
medical
resource,
machine
learning
prediction
algorithms.
To
best
our
knowledge,
first
research
study
that
maps
translated
texts
external
resources
improve
accuracy
model.
model
consists
four
phases.
Firstly,
NLP-based
data
preprocessing
pipeline
employed
ensure
input
dataset
suitable
format
analysis.
Secondly,
ChatGPT
transformer
utilized
translate
tweets
into
English,
enabling
further
processing
analysis
English.
Thirdly,
relevant
concepts
are
from
text
quickUMLS
tool
metathesaurus,
aiding
identifying
important
terms
related
health.
Fourthly,
TF-IDF
Bag
Words
(BOW)
algorithms
used
assign
weights
features,
highlighting
significance
concepts.
Finally,
classification
algorithms,
Support
Vector
Machine
(SVM),
Logistic
Regression
(LR),
Random
Forest
(RF),
Naive
Bayes
(NB),
Stochastic
Gradient
Descent
(SGD),
trained
Among
classifiers,
with
demonstrated
performance,
achieving
80.24%.
Indonesian Journal of Computer Science,
Год журнала:
2024,
Номер
13(3)
Опубликована: Июнь 15, 2024
In
the
landscape
of
digital
communication,
sentiment
analysis
stands
out
as
a
pivotal
technology
for
deciphering
vast
troves
unstructured
text
generated
online.
When
integrated
with
machine
learning,
transforms
into
powerful
tool
capable
distilling
insights
from
complex
human
emotions
and
opinions
expressed
across
social
media,
reviews,
forums.
This
review
paper
embarks
on
thorough
exploration
integration
learning
techniques
analysis,
shedding
light
latest
advancements,
challenges,
applications
spanning
various
sectors
including
public
health,
finance,
consumer
behavior.
It
meticulously
examines
role
in
elevating
through
improved
accuracy,
adaptability,
depth
analysis.
Furthermore,
discusses
implications
these
technologies
understanding
sentiment,
tracking
health
trends,
forecasting
market
movements.
By
synthesizing
findings
seminal
studies
cutting-edge
research,
this
not
only
charts
current
but
also
forecasts
trajectory
underscores
necessity
ongoing
innovation
models
to
keep
pace
evolving
discourse.
The
presented
herein
aim
guide
future
research
endeavors,
highlight
transformative
impact
outline
potential
new
that
could
benefit
society
at
large.