The Use of Machine Learning in Real-World Data: A Systematic Review of Disease Prediction and Management (Preprint)
Published: Nov. 17, 2024
BACKGROUND
Machine
learning
(ML)
and
big
data
analytics
are
revolutionizing
healthcare,
particularly
in
disease
prediction,
management,
personalized
care.
With
vast
amounts
of
real-world
(RWD)
from
sources
like
electronic
health
records
(EHRs),
patient
registries,
wearable
devices,
ML
offers
significant
potential
to
improve
clinical
outcomes.
However,
quality,
transparency,
integration
challenges
remain.
OBJECTIVE
This
study
aims
systematically
review
the
use
for
prediction
identifying
most
common
methods,
types,
designs,
evidence
(RWE).
METHODS
A
systematic
followed
PRISMA
guidelines
identify
studies
that
utilized
machine
methods
analyzing
management.
The
focused
on
extracting
related
algorithms
used,
categories,
types
studies,
RWE,
such
as
devices.
RESULTS
revealed
frequently
employed
were
Random
Forest
(RF),
Logistic
Regression
(LR),
Support
Vector
(SVM).
These
applied
across
various
with
cardiovascular
diseases,
cancers,
neurological
disorders
being
common.
Real-world
primarily
originated
EHRs,
a
predominant
focus
predictive
modeling
CONCLUSIONS
hold
promise
enhancing
healthcare
through
better
model
interpretability,
generalizability
must
be
addressed
integrate
models
fully
into
practice.
Language: Английский
The Use of Machine Learning in Real-World Data: A Systematic Review of Disease Prediction and Management (Preprint)
JMIR Medical Informatics,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 17, 2024
Language: Английский