Clinical Chemistry and Laboratory Medicine (CCLM),
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 5, 2024
In
the
last
decades,
clinical
laboratories
have
significantly
advanced
their
technological
capabilities,
through
use
of
interconnected
systems
and
software.
Laboratory
Information
Systems
(LIS),
introduced
in
1970s,
transformed
into
sophisticated
information
technology
(IT)
components
that
integrate
with
various
digital
tools,
enhancing
data
retrieval
exchange.
However,
current
capabilities
LIS
are
not
sufficient
to
rapidly
save
extensive
data,
generated
during
total
testing
process
(TTP),
beyond
just
test
results.
This
opinion
paper
discusses
qualitative
types
TTP
proposing
how
divide
laboratory-generated
two
categories,
namely
metadata
peridata.
Being
both
peridata
derived
from
process,
it
is
proposed
first
useful
describe
characteristics
while
second
for
interpretation
Together
standardizing
preanalytical
coding,
subdivision
or
might
enhance
ML
studies,
also
by
facilitating
adherence
laboratory-derived
Findability,
Accessibility,
Interoperability,
Reusability
(FAIR)
principles.
Finally,
integrating
can
improve
usability,
support
utility,
advance
AI
model
development
healthcare,
emphasizing
need
standardized
management
practices.
TrAC Trends in Analytical Chemistry,
Год журнала:
2024,
Номер
179, С. 117872 - 117872
Опубликована: Июль 15, 2024
Precision
medicine,
utilizing
genomic
and
phenotypic
data,
aims
to
tailor
treatments
for
individual
patients.
However,
successful
implementation
into
clinical
practice
is
challenging.
Machine
learning
(ML)
algorithms
have
demonstrated
incredible
capabilities
in
handling
probabilities,
managing
diverse
datasets,
are
increasingly
applied
precision
medicine
research.
The
key
ML
applications
include
classification
diagnosis,
patient
stratification,
prognosis,
treatment
monitoring.
offers
solutions
automated
structural
elucidation,
silico
library
construction,
efficient
processing
of
mass
spectrometry
raw
data.
Integration
with
genome-scale
metabolic
models
(GEMs)
provides
mechanistic
insights
genotype-phenotype
relationships.
In
this
manuscript,
we
examine
the
impact
various
facets
from
diagnostics
phenotyping
personalized
strategies.
Finally,
propose
a
methodological
framework
implementing
practice,
emphasizing
step-by-step
approach,
starting
identification
needs
research
questions,
followed
by
development,
validation,
implementation.
Health care science,
Год журнала:
2024,
Номер
3(5), С. 360 - 364
Опубликована: Окт. 1, 2024
In
this
commentary,
we
elucidate
three
indispensable
evaluation
steps
toward
the
real-world
deployment
of
machine
learning
within
healthcare
sector
and
demonstrate
referable
examples
for
diagnostic,
therapeutic,
prognostic
tasks.
We
encourage
researchers
to
move
beyond
retrospective
within-sample
validation,
step
into
practical
implementation
at
bedside
rather
than
leaving
developed
models
in
dust
archived
literature.
Machine
(ML)
has
been
increasingly
used
tackling
various
tasks
owing
its
capability
learn
reason
without
explicit
programming
[1].
Most
ML
have
had
their
accuracy
proven
through
internal
validation
using
data.
However,
external
data,
continual
monitoring
prospective
randomized
controlled
trials
(RCTs)
data
are
important
translation
clinical
practice
[2].
Furthermore,
ethics
fairness
across
subpopulations
should
be
considered
throughout
these
evaluations.
Different
from
which
evaluates
performance
a
subset
original
datasets,
assesses
contexts
that
may
vary
subtly
or
considerably
one
they
were
[3].
External
serves
rectify
inflated
estimates
capabilities
overfitting
guarantees
generalizability
transportability
diverse
populations
[4].
For
can
leverage
abundant
resources
publicly
accessible
databases
such
as
PhysioNet
[5].
Three
scenarios
recommended
after
identifying
suitable
database
with
sufficient
sample
size
guarantee
testing
robustness
[6].
The
first
involves
directly
deploying
trained
on
simulate
brand-new
scenario
previous
second
entails
large
training
set
new
fine-tune
models,
simulating
ample
collected
context
[7].
third
represents
an
intermediate
situation
wherein
gradually
fed
where
deployed
setting,
incrementally
collected,
updated
iteratively
newly
[8].
existing
studies
focused
direct
[9].
Holsbeke
et
al.
[10]
previously
published
diagnostic
detecting
adnexal
mass
malignancy
multiple
medical
centers
different
countries
population
characteristics.
therapeutic
pertinent
reference
is
study
investigating
survival
benefits
adjuvant
therapy
breast
cancer
evaluated
originally
United
Kingdom,
settings
States
[11].
realm
tasks,
Clift
[12]
offered
comprehensive
approach
externally
validate
predicting
10-year
risk
cancer-related
mortality,
detailing
methods
calculation,
identification,
outcome
definition,
evaluation.
addition
assessing
model
performance,
similarity
between
datasets
quantified
enable
elucidation
degradation
further
identify
potential
avenues
enhancement
[13].
Following
large-scale
subsequent
specific
setting
[14].
Specifically,
receive
make
predictions
accordingly,
predefined
time
frame
Compared
step,
distribution
drift,
control
quality,
trigger
system
alarms
when
deviates
normal
behavior
target
[15].
Because
operation
mainly
conducted
by
professionals,
developers
focus
user-friendly
practice.
aspect
offline
hospital
allocated
computation
would
limited
low
latency
responding
other
functions
inside
system.
development
secure
privacy-aware
maintenance
method
quickly
addressing
technical
collapses
while
minimizing
access
patients'
private
last
interface
Android
app
[16]
web-based
software
[17]
facilitates
use
health
care
professionals
comprehends
suggestions.
It
emphasized
application
designed
operate
independently
from,
not
interfere
with,
decision-making
processes.
This
precaution
necessary
avoid
any
adverse
impact
quality.
Exemplary
seen
work
Wissel
[18].
Those
authors
prospective,
real-time
assessment
ML-based
classifiers
epilepsy
surgery
candidacy
Cincinnati
Children's
Hospital
Medical
Center.
To
mitigate
risks
associated
classifiers,
patients
who
deemed
appropriate
surgical
candidates
algorithm
subjected
manual
review
two
expert
epileptologists,
final
decisions
confirmed
via
chart
review.
A
critical
insight
was
effective
necessitates
synergistic
collaboration
clinicians,
provide
essential
expertize,
information
technology
contribute
research
operational
knowledge
[19,
20].
Assuming
tool
demonstrates
accurate
pursue
approval
RCTs
administrative
committees.
tools
classic
four-phase
RCTs.
ensure
safety
real-life
scenarios,
absolutely
interventions
likely
avoided.
recommend
designing
compare
diagnosis
clinicians
(intervention
group)
(control
[21-23].
instance,
He
[24]
implemented
ML-guided
workflows
reduced
required
sonographers
cardiologists
diagnoses
left
ventricular
ejection
fraction.
seek
ethical
institutional
board
comply
standards
regulations.
Then,
proceed
Phase
I
trial
assess
(whether
introduction
distracts
impairs
diagnoses)
used.
II,
few
hundred
recruited
whether
statistically
significant
improvements
result
clinicians'
diagnoses.
III,
several
even
thousand
effectiveness
tool,
demonstrating
superiority
over
solutions.
If
receives
agency
then
investigate
wider
range
IV.
Upon
efficacy
rigorously
RCTs,
national
regulatory
agencies
US
Food
Drug
Administration
(FDA)
commercialization
[25].
paradigmatic
illustration
found
Titano
[26].
three-dimensional
convolutional
neural
networks
diagnose
acute
neurological
events
head
computed
tomography
images.
efficiency
subsequently
validated
randomized,
double-blind,
trial.
suggest
referring
Nimri
[27].
multicenter
multinational
physicians
specialized
academic
diabetes
optimizing
insulin
pump
doses.
Mayo
Clinic
1-year
occurrence
asthma
exacerbation
[28].
detailed
guideline
conducting
could
benefit
FDA's
Policy
Device
Software
Functions
Mobile
Applications
[29],
includes
provisions
applications
apply
algorithms
[30].
Alongside
population-level
evaluations,
there
burgeoning
awareness
about
implications
revealed
diagnose,
treat,
bill
inconsistently
[31].
Therefore,
it
imperative
equity
patient
outcomes,
resource
allocation
[31-33].
Thompson
[34]
proposed
framework
biases
recalibration
modules.
module
adjusted
decision
cutoff
threshold
affected
bias,
recalibrated
outputs,
enhancing
congruence
observed
events.
Chen
[31]
systematically
summarized
path
fair
medicine,
subpopulation
collection
federated
learning,
principles,
operationalization
ecosystems,
independent
regularization
governance
disparities.
Apart
assessments,
endorsement
thoroughly
integrated
processes
[31,
35].
light
these,
buried
Han
Yuan:
Conceptualization
(lead);
curation
formal
analysis
investigation
methodology
writing—original
draft
writing—review
editing
(lead).
like
acknowledge
Prof.
Nan
Liu
Duke-NUS
School
his
invaluable
support.
author
declares
no
conflict
interest.
exempt
committee
because
did
involve
human
participants,
animal
subjects,
sensitive
collection.
Not
applicable.
Data
sharing
applicable
article
generated
analyzed
during
current
study.
Translational Vision Science & Technology,
Год журнала:
2024,
Номер
13(4), С. 4 - 4
Опубликована: Апрель 2, 2024
Purpose:
Establishing
a
development
environment
for
machine
learning
is
difficult
medical
researchers
because
to
code
major
barrier.
This
study
aimed
improve
the
accuracy
of
postoperative
vault
value
prediction
model
implantable
collamer
lens
(ICL)
sizing
using
without
coding
experience.
Methods:
We
used
Orange
data
mining,
recently
developed
open-source,
code-free
tool.
included
eye-pair
from
294
patients
B&VIIT
Eye
Center
and
26
Kim's
Hospital.
The
was
OCULUS
Pentacam
internally
evaluated
through
10-fold
cross-validation.
External
validation
performed
Results:
successfully
trained
collected
coding.
random
forest
showed
mean
absolute
errors
124.8
µm
152.4
internal
cross-validation
external
validation,
respectively.
For
high
(>750
µm),
areas
under
curve
0.725
0.760
datasets,
better
than
classic
statistical
regression
models
Google
no-code
platform.
Conclusions:
Applying
tool
our
ICL
implantation
datasets
more
accurate
models.
Translational
Relevance:
Because
significant
bias
in
measurements
surgery
between
clinics,
customized
nomogram
will
implantation.
Clinical Chemistry and Laboratory Medicine (CCLM),
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 5, 2024
In
the
last
decades,
clinical
laboratories
have
significantly
advanced
their
technological
capabilities,
through
use
of
interconnected
systems
and
software.
Laboratory
Information
Systems
(LIS),
introduced
in
1970s,
transformed
into
sophisticated
information
technology
(IT)
components
that
integrate
with
various
digital
tools,
enhancing
data
retrieval
exchange.
However,
current
capabilities
LIS
are
not
sufficient
to
rapidly
save
extensive
data,
generated
during
total
testing
process
(TTP),
beyond
just
test
results.
This
opinion
paper
discusses
qualitative
types
TTP
proposing
how
divide
laboratory-generated
two
categories,
namely
metadata
peridata.
Being
both
peridata
derived
from
process,
it
is
proposed
first
useful
describe
characteristics
while
second
for
interpretation
Together
standardizing
preanalytical
coding,
subdivision
or
might
enhance
ML
studies,
also
by
facilitating
adherence
laboratory-derived
Findability,
Accessibility,
Interoperability,
Reusability
(FAIR)
principles.
Finally,
integrating
can
improve
usability,
support
utility,
advance
AI
model
development
healthcare,
emphasizing
need
standardized
management
practices.