Deep study on autonomous learning techniques for complex pattern recognition in interconnected information systems
Computer Science Review,
Год журнала:
2024,
Номер
54, С. 100666 - 100666
Опубликована: Сен. 20, 2024
Язык: Английский
Genetic factors, risk prediction and AI application of thrombotic diseases
Experimental Hematology and Oncology,
Год журнала:
2024,
Номер
13(1)
Опубликована: Авг. 27, 2024
Abstract
In
thrombotic
diseases,
coagulation,
anticoagulation,
and
fibrinolysis
are
three
key
physiological
processes
that
interact
to
maintain
blood
in
an
appropriate
state
within
vessels.
When
these
become
imbalanced,
such
as
excessive
coagulation
or
reduced
anticoagulant
function,
it
can
lead
the
formation
of
clots.
Genetic
factors
play
a
significant
role
onset
diseases
exhibit
regional
ethnic
variations.
The
decision
whether
initiate
prophylactic
therapy
is
matter
clinicians
must
carefully
consider,
leading
development
various
risk
assessment
scales
clinical
practice.
Given
considerable
heterogeneity
diagnosis
treatment,
researchers
exploring
application
artificial
intelligence
medicine,
including
disease
prediction,
diagnosis,
prevention,
patient
management.
This
paper
reviews
research
progress
on
genetic
involved
analyzes
advantages
disadvantages
commonly
used
characteristics
ideal
scoring
scales,
explores
medical
field,
along
with
its
future
prospects.
Язык: Английский
Neurodegenerative disorders: A Holistic study of the explainable artificial intelligence applications
Engineering Applications of Artificial Intelligence,
Год журнала:
2025,
Номер
153, С. 110752 - 110752
Опубликована: Апрель 18, 2025
Язык: Английский
Multiple feature selection based on an optimization strategy for causal analysis of health data
Health Information Science and Systems,
Год журнала:
2024,
Номер
12(1)
Опубликована: Ноя. 12, 2024
Recent
advancements
in
information
technology
and
wearable
devices
have
revolutionized
healthcare
through
health
data
analysis.
Identifying
significant
relationships
complex
enhances
public
strategies.
In
analytics,
causal
graphs
are
important
for
investigating
the
among
features.
However,
they
face
challenges
owing
to
large
number
of
features,
complexity,
computational
demands.
Feature
selection
methods
useful
addressing
these
challenges.
this
paper,
we
present
a
framework
multiple
feature
based
on
an
optimization
strategy
analysis
data.
Язык: Английский