Enhancing Real-Time Patient Monitoring in Intensive Care Units with Deep Learning and the Internet of Things
Big Data,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 17, 2025
The
demand
for
intensive
care
units
(ICUs)
is
steadily
increasing,
yet
there
a
relative
shortage
of
medical
staff
to
meet
this
need.
Intensive
work
inherently
heavy
and
stressful,
highlighting
the
importance
optimizing
these
units'
working
conditions
processes.
Such
optimization
crucial
enhancing
efficiency
elevating
level
diagnosis
treatment
provided
in
ICUs.
intelligent
ICU
concept
represents
novel
ward
management
model
that
has
emerged
through
advancements
modern
science
technology.
This
includes
communication
technology,
Internet
Things
(IoT),
artificial
intelligence
(AI),
robotics,
big
data
analytics.
By
leveraging
technologies,
aims
significantly
reduce
potential
risks
associated
with
human
error
improve
patient
monitoring
outcomes.
Deep
learning
(DL)
IoT
technologies
have
huge
revolutionize
surveillance
patients
ICUs
due
critical
complex
nature
their
conditions.
article
provides
an
overview
most
recent
research
applications
linical
critically
ill
patients,
focus
on
execution
AI.
In
ICU,
seamless
continuous
critical,
as
even
little
delays
decision-making
can
result
irreparable
repercussions
or
death.
looks
at
how
like
DL
monitoring,
clinical
results,
Furthermore,
it
investigates
function
wearable
advanced
health
sensors
coupled
networking
systems,
which
enable
secure
connection
analysis
various
forms
predictive
remote
by
professionals.
assessing
existing
outlining
roles
IoT,
analyzing
benefits
limitations
integration,
study
hopes
shed
light
future
identify
opportunities
further
research.
Language: Английский
21st century critical care medicine: An overview
World Journal of Critical Care Medicine,
Journal Year:
2024,
Volume and Issue:
13(1)
Published: March 5, 2024
Critical
care
medicine
in
the
21st
century
has
witnessed
remarkable
advancements
that
have
significantly
improved
patient
outcomes
intensive
units
(ICUs).
This
abstract
provides
a
concise
summary
of
latest
developments
critical
care,
highlighting
key
areas
innovation.
Recent
include
Precision
Medicine:
Tailoring
treatments
based
on
individual
characteristics,
genomics,
and
biomarkers
to
enhance
effectiveness
therapies.
The
objective
is
describe
recent
Care
Medicine.
Telemedicine:
integration
telehealth
technologies
for
remote
monitoring
consultation,
facilitating
timely
interventions.
Artificial
intelligence
(AI):
AI-driven
tools
early
disease
detection,
predictive
analytics,
treatment
optimization,
enhancing
clinical
decision-making.
Organ
Support:
Advanced
life
support
systems,
such
as
Extracorporeal
Membrane
Oxygenation
Continuous
Renal
Replacement
Therapy
provide
better
organ
support.
Infection
Control:
Innovative
infection
control
measures
combat
emerging
pathogens
reduce
healthcare-associated
infections.
Ventilation
Strategies:
ventilation
modes
lung-protective
strategies
minimize
ventilator-induced
lung
injury.
Sepsis
Management:
Early
recognition
aggressive
management
sepsis
with
tailored
Patient-Centered
Care:
A
shift
towards
patient-centered
focusing
psychological
emotional
well-being
addition
medical
needs.
We
conducted
thorough
literature
search
PubMed,
EMBASE,
Scopus
using
our
strategy,
incorporating
keywords
telemedicine,
management.
total
125
articles
meeting
criteria
were
included
qualitative
synthesis.
To
ensure
reliability,
we
focused
only
published
English
language
within
last
two
decades,
excluding
animal
studies,
Language: Английский
Artificial Intelligence in the Intensive Care Unit: Present and Future
Deleted Journal,
Journal Year:
2025,
Volume and Issue:
4, P. 464 - 464
Published: March 18, 2025
Introduction:
Artificial
intelligence
(AI)
is
significantly
transforming
critical
medicine
and
intensive
care.
Its
ability
to
process
large
volumes
of
data
generate
accurate
predictions
has
improved
medical
decision-making,
optimizing
diagnosis,
treatment,
reducing
the
workload
healthcare
personnel.
Methodology:
A
literature
review
was
conducted
between
November
2024
February
2025,
consulting
databases
such
as
SciELO,
LILACS,
Scopus,
PubMed-MedLine,
Google
Scholar,
ClinicalKeys.
Original
articles,
case
reports,
open-access
systematic
reviews
from
last
5
years
were
selected,
using
descriptors
in
Health
Sciences
(DeCS)
Boolean
operators
for
search.
Development:
Current
applications
AI
ICU
include:
Monitoring
early
detection
adverse
events
sensors
machine
learning
algorithms;
diagnosis
prognosis
through
deep
neural
networks
image
interpretation;
treatment
optimization,
including
adjustments
mechanical
ventilation
pharmacogenomics;
efficient
management
hospital
resources.
The
future
care
oriented
towards
more
explanatory
transparent
systems,
personalized
precision
medicine,
integration
with
emerging
technologies
automation
clinical
processes.
Conclusions:
redefining
units,
improving
diagnostic
accuracy,
treatments,
decision-making
thus
allowing
management.
However,
advanced
it
is,
will
never
replace
empathy
judgment
professionals.
By
integrating
responsibly,
we
not
only
save
lives,
but
also
humanize
patient
care,
always
remembering
that,
at
heart
there
compassion
commitment
each
patient.
Language: Английский
Predicting ICU Mortality Among Septic Patients Using Machine Learning Technique
Abdulla Al‐Ansari,
No information about this author
Fatima A. Bahman Nejad,
No information about this author
Roudha J. Al-Nasr
No information about this author
et al.
Journal of Clinical Medicine,
Journal Year:
2025,
Volume and Issue:
14(10), P. 3495 - 3495
Published: May 16, 2025
Introduction:
Sepsis
leads
to
substantial
global
health
burdens
in
terms
of
morbidity
and
mortality
is
associated
with
numerous
risk
factors.
It
crucial
identify
sepsis
at
an
early
stage
order
limit
its
escalation
sequelae
the
condition.
The
purpose
this
research
predict
ICU
evaluate
predictive
accuracy
machine
learning
algorithms
for
among
septic
patients.
Methods:
study
used
a
retrospective
cohort
from
computerized
records
accumulated
280
hospitals
between
2014
2015.
Initially
sample
size
was
23.47K.
Several
models
were
trained,
validated,
tested
using
five-fold
cross-validation,
three
sampling
strategies
(Under-Sampling,
Over-Sampling,
Combination).
Results:
under-sampled
approach
combined
augmentation
Extra
Trees
model
produced
best
performance
Accuracy,
Precision,
Sensitivity,
Specificity,
F1-Score,
AUC
90.99%,
84.16%,
94.89%,
88.48%,
89.20%,
91.69%,
respectively,
Top
30
features.
For
29
features
showed
82.99%,
51.38%,
71.72%,
85.41%,
59.87%,
78.56%,
respectively.
Down-Sampling,
31
81.78%,
49.08%,
79.76%,
82.21%,
60.76%,
80.98%,
Conclusions:
Machine
can
reliably
when
suitable
clinical
predictors
are
utilized.
that
proposed
90.99%
only
single-entry
data.
Incorporating
longitudinal
data
could
further
enhance
performance.
Language: Английский