Journal of Technology in Behavioral Science,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 28, 2024
Abstract
This
systematic
literature
review
explores
the
emerging
field
of
remote-based
deep
learning
predictive
algorithms
for
depression,
focusing
on
addressing
limitations
traditional
diagnostic
methods
and
examining
current
state
research
in
this
novel
area.
A
search
was
conducted
Embase,
Medline,
Web
Science
Core
Collection,
CINAHL,
PsycINFO
June
2023.
To
capture
relevant
studies,
titles
abstracts
papers
were
reviewed
against
predefined
inclusion
exclusion
criteria
using
four
groups
keywords
prediction,
validity,
learning.
Eligible
studies
systematically
based
Critical
Appraisal
Data
Extraction
Systematic
Reviews
Prediction
Modelling
Studies
(CHARMS)
checklist.
The
risk
bias
assessed
Model
Risk
Bias
Assessment
(PROBAST)
Tool
methodological
quality.
synthesis
data
Synthesis
Without
Meta-Analysis
(SWiM)
framework.
From
286
initially
identified,
6
met
all
criteria,
published
between
2020
Performance
metrics
revealed
potential
models,
with
accuracy
rates
reaching
as
high
98.23%.
Convolutional
neural
networks
(CNNs)
emerged
predominant
model,
applicability
across
diverse
sources
such
speech
recordings,
body
motion
data,
facial
images.
However,
issues
related
to
prevalent,
most
lacking
essential
reporting
details
employing
relatively
small
sample
sizes.
identified
practical
application
these
including
limited
demographic
representation,
absence
external
validation,
a
notable
models
capable
anticipating
onset
depression.
While
focus
primarily
identifying
existing
depression
any
duration,
there
is
need
advancements
that
enable
anticipation
future
depressive
episodes.
advance
field,
we
recommend
standardized
practices,
larger
more
datasets,
development
anticipate
occurrences
advance.
These
enhancements
will
contribute
credibility
real-world
relevance
models.
hold
promise
revolutionizing
they
require
refinement
validation
fulfil
their
clinical
practice.
underscores
further
area
address
improved
mental
health
assessment
intervention.
Sensors,
Journal Year:
2025,
Volume and Issue:
25(2), P. 567 - 567
Published: Jan. 19, 2025
The
objective
identification
of
depression
using
physiological
data
has
emerged
as
a
significant
research
focus
within
the
field
psychiatry.
advancement
wearable
measurement
devices
opened
new
avenues
for
individuals
with
in
everyday-life
contexts.
Compared
to
other
methods,
wearables
offer
potential
continuous,
unobtrusive
monitoring,
which
can
capture
subtle
changes
indicative
depressive
states.
present
study
leverages
multimodal
wristband
collect
from
fifty-eight
participants
clinically
diagnosed
during
their
normal
daytime
activities
over
six
hours.
Data
collected
include
pulse
wave,
skin
conductance,
and
triaxial
acceleration.
For
comparison,
we
also
utilized
matched
healthy
controls
publicly
available
dataset,
same
equivalent
durations.
Our
aim
was
identify
through
analysis
measurements
derived
daily
life
scenarios.
We
extracted
static
features
such
mean,
variance,
skewness,
kurtosis
indicators
like
heart
rate,
acceleration,
well
autoregressive
coefficients
these
signals
reflecting
temporal
dynamics.
Utilizing
Random
Forest
algorithm,
distinguished
non-depressive
varying
classification
accuracies
on
aggregated
6
h,
2
30
min,
5
min
segments,
90.0%,
84.7%,
80.1%,
76.0%,
respectively.
results
demonstrate
feasibility
wearable-derived
recognition.
achieved
suggest
that
this
approach
could
be
integrated
into
clinical
settings
early
detection
monitoring
symptoms.
Future
work
will
explore
methods
personalized
interventions
real-time
offering
promising
avenue
enhancing
mental
health
care
integration
technology.
PNAS Nexus,
Journal Year:
2025,
Volume and Issue:
4(2)
Published: Feb. 1, 2025
Abstract
Acute
liver
failure
(ALF)
is
a
serious
disease
that
progresses
from
acute
injury
(ALI)
and
often
leads
to
multiorgan
ultimately
death.
Currently,
effective
treatment
strategies
for
ALF,
aside
transplantation,
remain
elusive,
partly
because
ALI
highly
heterogeneous.
Furthermore,
clinicians
lack
quantitative
indicator
they
can
use
predict
which
patients
hospitalized
with
will
progress
ALF
the
need
transplantation.
In
our
study,
we
retrospectively
analyzed
data
319
admitted
hospital
ALI.
By
applying
machine-learning
approach
by
using
SHapley
Additive
exPlanations
(SHAP)
algorithm
analyze
time-course
blood
test
data,
identified
prothrombin
time
activity
percentage
(PT%)
as
biomarker
reflecting
individual
status.
Unlike
previous
studies
predicting
transplantation
in
study
focused
on
PT%
dynamics.
Use
of
this
variable
allowed
us
stratify
heterogeneous
into
six
groups
distinct
clinical
courses
prognoses,
i.e.
self-limited,
intensive
care–responsive,
or
care–refractory
patterns.
Notably,
these
were
well
predicted
collected
at
admission.
Additionally,
utilizing
mathematical
modeling
machine
learning,
assessed
predictability
dynamics
during
early
phase
Our
findings
may
allow
optimizing
medical
resource
allocation
introduction
tailored
individualized
treatment,
result
improving
prognosis.
Machine
learning
models
have
recently
become
popular
in
psychological
research.
However,
many
machine
lack
interpretable
parameters
that
researchers
from
psychology
are
used
to
parametric
models,
such
as
linear
or
logistic
regression.
To
gain
insights
into
how
the
model
has
made
its
predictions,
different
interpretation
techniques
been
proposed.
In
this
article,
we
focus
on
two
local
widely
learning:
Local
Interpretable
Model-Agnostic
Explanations
(LIME)
and
Shapley
values.
LIME
aims
at
explaining
predictions
close
neighborhood
of
a
specific
person.
values
can
be
understood
measure
predictor
relevance
contribution
variables
for
persons.
Using
illustrative,
simulated
examples,
explain
idea
behind
Shapley,
demonstrate
their
characteristics,
discuss
challenges
might
arise
application
interpretation.
For
LIME,
choice
size
may
impact
conclusions.
values,
show
they
interpreted
individually
person
interested
jointly
across
The
aim
article
is
support
safely
use
these
themselves,
but
also
critically
evaluate
interpretations
when
encounter
research
articles.
Biological Psychiatry Global Open Science,
Journal Year:
2024,
Volume and Issue:
4(6), P. 100366 - 100366
Published: July 20, 2024
Current
phenotyping
approaches
for
murine
autism
models
often
focus
on
one
selected
behavioral
feature,
making
the
translation
onto
a
spectrum
of
autistic
characteristics
in
humans
challenging.
Furthermore,
sex
and
environmental
factors
are
rarely
considered.
Here,
we
aimed
to
capture
full
manifestations
three
mouse
develop
"behavioral
fingerprint"
that
takes
influences
under
consideration.
To
this
end,
employed
wide
range
classical
standardized
tests;
two
multi-parametric
assays:
Live
Mouse
Tracker
Motion
Sequencing
(MoSeq),
male
female
Shank2,
Tsc1
Purkinje
cell
specific-Tsc1
mutant
mice
raised
standard
or
enriched
environments.
Our
aim
was
integrate
our
high
dimensional
data
into
single
platform
classify
differences
all
experimental
groups
along
dimensions
with
maximum
discriminative
power.
Multi-parametric
assays
enabled
far
more
accurate
classification
compared
tests,
dimensionality
reduction
analysis
demonstrated
significant
additional
gains
accuracy,
highlighting
presence
sex,
genotype
groups.
Together,
results
provide
complete
phenotypic
description
tested
groups,
suggesting
can
entire
heterogenous
phenotype
models.
