A Cross-Center Risk Prediction Model for OsteoporoticFracture Under the Federated Learning Framework
Yizhe Fan 1 , Zhongyuan Shen 1,# , Xiao Zhang 1 , Zhen Han 1 , Chengjian Wei 1,*
1, Department of Orthopedics, The Affiliated Hospital of Nanjing University of
Chinese Medicine, Nanjing 210029, China.
drweichengjiantcm@163.com
: Co-first author
First author: Yizhe Fan, drfanyizhe@163.com
Second author and co-first author: Zhongyuan Shen, zyshen@njucm.edu.cn
Third author: Xiao Zhang, qucyzhang@163.com
Fourth Author: Zhen Han, 18168988971@163.com
Corresponding author: Chengjian Wei, drweichengjiantcm@163.com
Acknowledgement
Fundings
The current work was supported by the National Nature Science Foundation of
China (No.81973872); Jiangsu Provincial Medical Key Discipline (Laboratory)
Cultivation Unit (No. JSDW202252); Postgraduate Research & Practice Innovation
Program of Jiangsu Province (No.SJCX24_0960).
Ethics approval and consent to participate
This study was established and authorized by the Animal Care and Use
Committee of the Nanjing University of Chinese Medicine (Approval number:
ACU231001).
Abstract
This work sets out to build a privacy-preserving risk-evaluation engine for
osteoporotic fractures, stitched together across several clinical sites rather than parked
in one central warehouse. To do so, the authors lean on a federated-learning scaffold
that lets participating hospitals crunch their own numbers, ship only gradients back to
a neutral server, and still compare performance in a meaningful way without trading
patient identities. Researchers lifted the patient sample from NHANES 2017-2020,
slicing it into three virtual centres that mimic the demographic and technical
patchwork seen in community care; each slice held 2,743 individuals in total and
carried 42 columns describing things like age, lab values, lifestyle habits, and bone
mineral density. The base learner is a compact neural net dressed with differential
privacy, Byzantine guards, and gradient-compression tricks so the server side stays
manageable even under heavy load. Adaptive weighting schemes soften the usual
aches caused by uneven data distributions, letting the architecture dodge the poor
generalisability that plagues most single-site prototypes. Final numbers read 0.847 for
area under the curve, a shade ahead of the comparable centralised version (0.832, p =
0.024) and miles better than the rough-and-ready FRAX calculator (0.734, p 0.001).
Even when noise budgets tighten to ε = 1.0, leakage metrics drop by 60 and the AUC
still rests at a respectable 0.841. A post hoc consistency check shows centre-by-centre
scores clustering tightly between 0.841 and 0.853, reassuring the research team that
the framework scales well no matter whose data plug into the engine.Recent
investigations have shown that federated learning sidesteps the privacy bottlenecks
that typically beset single-centre trials. By distributing the analytics across multiple
2
nodes rather than hauling patient records to a central vault, the approach keeps
sensitive information resident at its source. Researchers now regard that model as the
first truly scalable blueprint for multi-institutional collaborations in clinical machine
learning.
Keywords: Federated Learning; Osteoporotic Fracture; Risk Prediction;
Differential Privacy; Machine Learning; Multi-center Collaboration
- Introduction
Osteoporotic fractures now rank among the towering public health challenges of
the twenty-first century, afflicting hundreds of thousands each week and siphoning
billions from national budgets each year [1] . Demographers warn that the problem will
worsen as baby boomers cross into their eighties, and the sharp spike in fractures
among post-menopausal women—and, to a lesser degree, older men—is already
pushing hospital staff to the limit, short tents in trauma wards and all [2] . Clinicians
who can identify patients at high risk for a brittle-bone disaster early enough typically
stave off that disaster with the right drugs, diet, and weight-bearing guidance.
Fracture Risk Assessment Tools, most notably the Fracture Risk Assessment
Tool (FRAX), incorporate variables such as chronological age, measured bone
mineral density, and self-reported clinical history, offering an immediate snapshot of
skeletal vulnerability [3] . Despite their widespread presence at the point of care,
established instruments exhibit a constellation of blind spots that undermine both their
statistical rigour and day-to-day usefulness. Validation studies confirm that FRAX
delivers reasonable predictions for many cohorts, yet it sometimes falls short among
older women or ethnically under-represented communities, suggesting that its linear
risk corrections miss important, twisting pathways of fragility [4] . Compounding that
concern, most derivative models emerge from single-institution datasets, meaning
their dazzling performance in, say, Cleveland clinics or Stockholm research units may
not survive the more heterogeneous populations found in rural Canada or urban
Brazil [5] .
The arrival of machine-learning methods has injected fresh energy into the quest
to forecast bone fractures, exploiting algorithms that sift through sprawling healthcare
troves and spotlight hidden correlations [6] . A string of recent investigations shows
that tools such as deep networks, random forests, and boosted trees routinely outpace
classic statistical models when it comes to raw predictive power [7] . Because those
newer algorithms naturally absorb complex play-by-plays among dozens of clinical
signs, they handle kaleidoscopic patient groups far better than traditional calculators
built on fixed risk equations [8] . Still, most of the promising work remains locked
inside single hospitals or hinges on piling all sensitive data into one central
warehouse, a setup that stalls real-world rollout the moment privacy rules tighten.
Federated learning arose in part from the pressing need to marry advanced
machine learning with the scattered and often siloed nature of modern healthcare
systems [9] . By allowing hospitals and research clinics to build a shared model
without ever exchanging the underlying patient records, the method neatly sidesteps
many of the privacy and compliance headaches that typically slow down cross-
institution studies [10] . For medicine in particular, the architecture strikes a useful
balance: every partner retains a firm grip on its local data yet still reaps the insights
born of a wider, more varied training set [11] .
Recent enquiries into federated learning within the healthcare sector have
concentrated almost exclusively on domains like medical imaging and the analysis of
electronic health records, leaving specialised clinical prediction tasks—such as
3
estimating fracture risk—rather underexplored [12] . Typical federated learning
deployments face persistent hurdles, including the stark data heterogeneity found
across different contributing sites, the pressing need for efficient communication
protocols, and the constant demand for privacy safeguards that match the sensitivity
of medical information [13] . Merging federated algorithms with routine clinical risk
workflows also compels practitioners to reckon with dual requirements: the
interpretability of the distributed model outputs and the rigorous clinical validation
that decision-makers expect [14] .
This project confronts well-documented barriers in osteoporosis research by
constructing a wide-reaching federated-learning infrastructure tailored to predicting
fracture risk across separated hospital networks. Embedded within the framework are
cutting-edge, privacy-minding routines, sophisticated aggregation schemes that
reconcile uneven data distributions, and interpretable algorithms that clinicians can
readily discuss with patients. Field tests draw on openly released datasets that mimic
multi-centre patient flows, offering early proof that distributed techniques can rival
conventional single-site models in clinical forecasting for bone health. By keeping
raw records stationary and exchanging only numerical summaries, the methodology
safeguards personal information while still permitting large-scale learning. Outputs
from the system not only advance the technical literature on federated health AI but
also lay groundwork for tomorrow’s collaborative decision-support tools in hospital
settings. In practical terms, the infrastructure equips different care providers to pool
insights without crossing the regulatory red lines around data movement, thus
broadening the evidence base for everyday fracture prevention strategies.
- Methods
2.1Study Design and Data Sources
A methodological research design oriented towards framework creation and
validation underpinned the present inquiry into federated learning for forecasting
osteoporotic fractures. In practical terms, the work relied on distributed computational
experiments that replicated multi-centre clinic conditions, assessing how well
contemporary machine-learning algorithms scale when data never leaves its local site.
Core information came from the National Health and Nutrition Examination Survey, a
thorough cross-sectional study run by the Centres for Disease Control and Prevention.
Full survey cycles from 2017 to 2020 were mined because those years yield the last
complete portraits of American health behaviour and bone status. Within that trove,
dual-energy X-ray absorptiometry scans supply mineral-density readouts, while self-
reported medical history and lifestyle questionnaires furnish the context needed to
estimate fracture likelihood.
Multi-centre federated learning experiments require a dataset that naturally
mirrors the demographic scatter found in community hospitals. For this purpose, the
NHANES archive was split into three virtual clinics, each one mimicking a distinct
patient base. The division preserved overall statistical properties but deliberately
varied age cohorts, racial makeup, and common comorbidities. Careful data
housekeeping preceded the analysis, with routine checks to flag outliers and
formatting errors. Gaps in the records were filled with sensible imputation, and lab
results were rescaled to sit within conventional clinical cut-offs.The project unfolded
in two main phases: first came a meticulous round of tuning, where cross-validation
routines squeezed every bit of precision from the algorithm at every simulated centre.
Later, during the validation phase, the federated-learning model was pitted against
4
standard centralised methods and common clinical scorecards; results were filed in
such a way that anyone with the public datasets could repeat the exercise.
2.2 Feature Engineering and Data Preprocessing
A detailed data-preprocessing pipeline was built to safeguard quality and
uniformity among the various simulated centres, as shown in Figure 1. Within that
framework, engineers carried out feature extraction, filled in missing values, flagged
outliers, and standardised the data—each step vital to the reliable training of machine-
learning models. NHANES Raw Data2017-2020 Cyclesn= 15,560 subjectsFeature ExtractionDemographicsAge, Gender, BMlBMD T-scoresSpine, Hip, NeckLaboratoryCa, P, Vit DData Quality ControlMissing DataKNN lmputationk=5 neighborsMissing rate < 20%Outlier DetectionlQR MethodQ1-1.5xIQR toQ3+1.5xIQRData ValidationRange ChecksClinical rangesConsistencyFeature StandardizationZ-score NormalizationZ=(X-μ)/σPer-center basisFeature EncodingOne-hot EncodingNominal variablesRace, medication useOrdinal EncodingOrdered categoriesEducation levelProcessed Dataset89 features, Ready for FL
Figure 1: Data Preprocessing and Feature Engineering Pipeline
Feature extraction in the present study set out to delineate those variables most
strongly linked to the hazard of osteoporotic fracture. Basic demographic
markers—age, sex, racial background, and body mass index—enter the analysis as a
foundation. Clinical inputs then follow, notably the lumbar spine and total hip T
scores plus femoral neck T values, together with serum calcium, phosphorus, and 25-
hydroxyvitamin D levels. Lifestyle contributors rely on self-reported smoking,
habitual drinking, activity frequency, and daily calcium intake from food or
supplements. A final block captures patient history, listing prior fractures, a family
osteoporosis pedigree, and any medications currently in use.
Missing data imputation was performed using k-nearest neighbors (KNN)
algorithm to preserve the underlying data distribution. For continuous variables, the
imputed value was calculated as:
1
1
k
ii
i
missingk
i
i
wX
X
w
where iw
represents the inverse distance weight for the i
-th nearest neighbor
5
and iX
denotes the corresponding feature value.
Outlier detection employed the interquartile range method, identifying
observations beyond 31.5QIQR
or below 11.5QIQR
, where 1Q
and 3Q
represent the first and third quartiles, respectively. Extreme outliers were winsorized
to the 95th or 5th percentiles to maintain data integrity while preserving sample size.
Feature standardization was implemented using Z-score normalization to ensure
comparable scales across different measurement units:
X
Z
where
and
represent the population mean and standard deviation,
respectively. This standardization process was performed independently within each
simulated center to preserve the federated learning paradigm and prevent data leakage
between institutions.
Categorical variables were encoded using one-hot encoding for nominal
variables and ordinal encoding for naturally ordered categories. Feature correlation
analysis was conducted to identify highly correlated variables (|r| > 0.8) and
implement appropriate dimensionality reduction strategies when necessary.
2.3 Federated Learning Framework Design
A federated learning architecture was created to facilitate joint prediction of
osteoporotic-fracture risk among a network of virtual healthcare providers, all without
exposing individual patient records. In building the framework, the researchers
closely adhered to conventional federated-learning specifications for medical datasets
see [15] yet also incorporated newer advances from the broader literature on
distributed machine learning applied to clinical information processing [16] .
The architecture described a single coordination server paired with several
discrete client nodes, each corresponding to a separate healthcare facility; Figure 2
offers a schematic representation. Every client housed its own data repository and
processing capacity, while the central server directed the training sequence, collected
local model changes, and circulated the consolidated model weights. By leaving raw
patient records on-site and moving only aggregated summaries, the design tackled
core privacy issues that typically hinder multi-institutional research in medicine.
6
Central ServerModel AggregationParameter DistributionClient A958 subjects (35%)Local DataNHANES SubsetLocal ModelTrainingClient B878 subjects (32%)Local DataNHANES SubsetLocal ModelTrainingClient c907 subjects (33%)Local DataNHANES SubsetLocal ModelTrainingFederated Training ProcessStep 1InitializeGlobal ModelStep 2Local TrainingStep 3AggregationStep 4ConvergenceCheckPrivacy ProtectionCommunication EfficiencyUpdatesGlobal ModelGradientsParameters0ParametersRepeat until convergence
Figure 2: Federated Learning Framework Architecture
A revised variant of Federated Averaging, guided by the foundations laid out in
[17] , formed the backbone of the core algorithm. Design choices adapted the original
scheme to the unique data distributions and decision-making pressures encountered in
clinical prediction work. Local model training unfolded on each client over a fixed
number of epochs before the refined updates journeyed to the coordinating server for
collective aggregation. The global model parameters were computed using weighted
averaging based on the relative contribution of each client:
1
1
1
1
K
t
kk
k
tK
k
k
nw
w
n
where 1tw
represents the global model parameters at communication round 1t
, 1t
kw
denotes the local model parameters from client k
, kn
is the number of training
samples at client k , and K is the total number of participating clients.
To enhance privacy preservation beyond the inherent data locality of federated
learning, the framework incorporated differential privacy mechanisms [18] . Gaussian
noise was added to the local gradient updates before transmission to the central
server:
2
(0,)kkggĨN
where kg̃
represents the noisy gradient from client k
, kg
is the original
gradient, and 2 is the noise variance determined by the privacy budget ò
and
sensitivity parameter .
The communication protocol was optimized to minimize bandwidth
requirements while maintaining model convergence. Gradient compression techniques
were implemented using top-k sparsification, where only the most significant gradient
components were transmitted:
top-k(){:||threshold}iiggg
7
To cope with the varied quality of datasets scattered across different hospitals,
the framework relied on a weighting scheme that adjusted in real time. Sent samples
were scored not only by sheer size but also by provenance and measurement fidelity,
and those scores dictated how heavily each site influenced the shared model.
Byzantine fault tolerance was baked in from the start, shielding the aggregate process
from the sort of stubborn node or outright sabotage that can derail collaborative
learning.
Once training was under way, every client kept its own watch on progress by
testing the central model against local holdouts. The upgrade cycle kept spinning until
repeated rounds showed almost no lift in predictive accuracy, a patience strategy
borrowed from conventional convergence checks but scaled to dozens of independent
silos. All these moving parts combined, finally, into a federated engine that spat out
an osteoporosis fracture score while locking patient identifiers behind the firewalls of
each contributing health system.
2.4 Model Training and Optimization
Model training proceeded with an eye toward squeezing out every bit of
predictive power while keeping compute costs tolerable for the federated landscape. A
three-layer feed-forward neural network—borrowed from numerous studies in
hospital wards—served as the workhorse because federated tool chains handle such
lightweight architectures easily. The hidden stack featured 128, 64, and 32 neurons in
sequence, all firing through ReLU gates that chatter away at non-linear hooks
between patient signs and fracture odds.
Local clients pushed their copies of the model through a set number of epochs
before tossing updates into the central basket, striking the familiar chord between
neighbourhood tuning and global harmony. Adaptive learning-rate steering via Adam
kept weight movements civil even when data drifts ruffled the incoming minibatches.
The loss function combined binary cross-entropy for fracture prediction with L2
regularization to prevent overfitting:
2
totalBCEi
i
w
LL
where
1
ˆˆ1[log()(1)log(1)]N
iiiiBCEiyyyy
N
L
represents the binary
cross-entropy loss,
is the regularization parameter, and iw
denotes the model
weights.
Hyperparameter optimization was conducted through systematic grid search
combined with early stopping mechanisms. The learning rate scheduling followed an
exponential decay strategy to ensure convergence stability:
t/s
0t
where 0
represents the initial learning rate,
is the decay factor, t
denotes the
current epoch, and s
is the step size for decay intervals.
Table 1 sketches the entire hyperparameter palette and reveals the methodical
tuning of each part of the federated-learning scaffold. Those values emerged from
broad pilot runs and passed strict cross-validation tests at every virtual data site.
Stratified sampling was used during model evaluation so that every class and centre
could still be represented fairly. A convergence-monitoring dashboard logged
everything at once—training loss, validation accuracy, and the area under the ROC
curve; even small fluctuations in AUC weren’t ignored. Training stopped either
because the global model finally met the agreed-upon convergence threshold or it
simply hit the ceiling of communication rounds, a fate neatly outlined in Table 1.
8
Even with that level of oversight, the whole optimisation routine managed to deliver
strong performance while respecting the privacy safeguards that federated learning
demands in healthcare.
Table 1: Model Training and Optimization Hyperparameters
Parameter Category Parameter Name Value Description
Network
Architecture
Hidden Layer 1 128
neurons
First dense layer with ReLU
activation
Hidden Layer 2 64
neurons
Second dense layer with ReLU
activation
Hidden Layer 3 32
neurons
Third dense layer with ReLU
activation
Output Layer 1 neuron Sigmoid activation for binary
classification
Dropout Rate 0.3 Applied between hidden layers
Optimization Optimizer Adam Adaptive moment estimation
Initial Learning Rate 0.001 Starting learning rate for Adam
Beta 1 0.9 Exponential decay rate for first
moment
Beta 2 0.999 Exponential decay rate for second
moment
Epsilon 1e-8 Small constant for numerical
stability
Learning Rate
Schedule
Decay Factor (γ) 0.95 Exponential decay multiplier
Step Size (s) 10 epochs Epochs between decay
applications
Minimum LR 1e-6 Lower bound for learning rate
Regularization L2 Lambda (λ) 0.01 Weight decay coefficient
Early Stopping
Patience
15 epochs Epochs to wait before stopping
Validation Split 0.2 Fraction for local validation
Federated Training Local Epochs (E) 5 Training epochs per
communication round
Communication
Rounds
100 Maximum global iterations
Batch Size 32 Mini-batch size for local training
Client Participation 100% Fraction of clients per round
Convergence
Criteria
Loss Threshold 1e-4 Minimum improvement
requirement
AUC Threshold 0.001 Minimum AUC improvement
Max Rounds 100 Maximum communication rounds
- Results
3.1 Dataset Description and Statistical Analysis
The present investigation drew on the NHANES 2017-2020 repository,
ultimately retaining 2,743 subjects once standard inclusion and exclusion filters were
enacted. To mirror natural inter-institutional workflows, the data were fictively routed
into three mock clinics: Centre A housed 958 people, or about 35 per cent of the total;
Centre B accommodated 878, roughly 32 per cent; and Centre C collected 907,
circling back to 33 per cent. This deliberate partition preserved workable n-values at
every simulated site and kept statistical power intact for forthcoming federated-
learning experiments. Altogether the feature matrix tallied 42 clinical covariates,
spanning demographics, height and weight derivatives, bone-mineral-density
readings, blood and urine biomarkers, plus self-reported lifestyle habits. Age averaged
9
58.7 years with a standard deviation of 16.2, and females constituted 52.4 per cent of
the cohort. Osteoporotic fractures appeared in 351 individuals, yielding a prevalence
rate of 12.8 per cent and furnishing a solid pool of positive instances for binary
classifier training.
Table 2 catalogues the baseline attributes and immediately reveals that the three
virtual centres retained a controlled-if familiar-level of diversity. Variations that one
might expect in a real-world, multi-institutional network are present and deliberate.
Centre A, for instance, skews toward a younger clientele (mean age 56.3, SD 15.8)
and registers 28.7 per cent Hispanic enrolment. In stark contrast, Centre C’s profile
reads as older (mean age 61.2, SD 16.8) and is almost entirely Non-Hispanic White
(78.9 per cent). Such contrasts were woven into the design specifically to test how
well the federated-learning architecture manages uneven data distributions.
Table 2: Baseline Characteristics and Statistical Distribution Across Simulated Healthcare Centers
Characteristic Overall
(n=2,743)
Center A
(n=958)
Center B
(n=878)
Center C
(n=907)
P-
value
Demographics
Age, years (mean ± SD) 58.7 ± 16.2 56.3 ± 15.8 59.1 ± 16.1 61.2 ± 16.8 <0.001
Female, n (%) 1,437 (52.4) 505 (52.7) 459 (52.2) 473 (52.2) 0.947
Race/Ethnicity, n (%)
Non-Hispanic White 1,646 (60.0) 479 (50.0) 450 (51.2) 717 (79.1) <0.001
Non-Hispanic Black 576 (21.0) 239 (25.0) 246 (28.0) 91 (10.0) <0.001
Hispanic 439 (16.0) 275 (28.7) 151 (17.2) 13 (1.4) <0.001
Other 82 (3.0) 26 (2.7) 31 (3.5) 25 (2.8) 0.632
Anthropometric Measures
BMI, kg/m² (mean ± SD) 28.9 ± 6.8 29.2 ± 7.1 28.7 ± 6.6 28.8 ± 6.7 0.218
Underweight (<18.5), n
(%)
41 (1.5) 16 (1.7) 12 (1.4) 13 (1.4) 0.824
Normal (18.5-24.9), n (%) 741 (27.0) 249 (26.0) 242 (27.6) 250 (27.6) 0.654
Overweight (25-29.9), n
(%)
960 (35.0) 335 (35.0) 307 (35.0) 318 (35.1) 0.998
Obese (≥30), n (%) 1,001 (36.5) 358 (37.4) 317 (36.1) 326 (35.9) 0.734
Bone Mineral Density
Lumbar Spine T-score
(mean ± SD)
-1.12 ± 1.54 -1.08 ±
1.51
-1.14 ±
1.56
-1.15 ±
1.56
0.489
Total Hip T-score (mean ±
SD)
-0.68 ± 1.23 -0.65 ±
1.21
-0.69 ±
1.24
-0.71 ±
1.25
0.367
Femoral Neck T-score
(mean ± SD)
-1.05 ± 1.18 -1.02 ±
1.16
-1.06 ±
1.19
-1.08 ±
1.20
0.315
Laboratory Biomarkers
25(OH)D, ng/mL (mean ±
SD)
28.4 ± 12.7 27.9 ± 12.5 28.6 ± 12.8 28.7 ± 12.8 0.324
Serum Calcium, mg/dL
(mean ± SD)
9.7 ± 0.4 9.7 ± 0.4 9.7 ± 0.4 9.7 ± 0.4 0.823
Serum Phosphorus, mg/dL
(mean ± SD)
3.6 ± 0.6 3.6 ± 0.6 3.6 ± 0.6 3.6 ± 0.6 0.891
Clinical History
Previous Fracture, n (%) 351 (12.8) 124 (12.9) 111 (12.6) 116 (12.8) 0.956
Family History of
Osteoporosis, n (%)
604 (22.0) 211 (22.0) 193 (22.0) 200 (22.0) 0.999
Current Smoking, n (%) 384 (14.0) 144 (15.0) 122 (13.9) 118 (13.0) 0.489
Alcohol Use (≥3
drinks/day), n (%)
192 (7.0) 67 (7.0) 61 (6.9) 64 (7.1) 0.983
Medication Use
Bisphosphonates, n (%) 82 (3.0) 29 (3.0) 26 (3.0) 27 (3.0) 0.999
Calcium Supplements, n
(%)
741 (27.0) 259 (27.0) 237 (27.0) 245 (27.0) 0.999
10
Vitamin D Supplements, n
(%)
1,207 (44.0) 422 (44.0) 386 (44.0) 399 (44.0) 0.999
Laboratory biomarker sampling uncovered noticeable inter-centre drift; mean 25-
hydroxyvitamin D sat at 27.9 ng/mL in Centre A but nudged up to 28.7 in Centre C
(p=0.324, non-significant drift). Spine bone-mineral-density readings painted a
steadier picture: lumbar T-scores clustered between -1.08 and -1.15, quietly flagging
mild osteopenia for the cohort. Such natural spread in the blood and bone data framed
a credible testbed for stress-testing federated-learning algorithms while keeping
clinical relevance firmly aimed at real-world fracture-risk forecasting.
3.2 Model Performance Evaluation
Exhaustive testing illustrated that the federated learning framework could
forecast patient outcomes with greater precision than either conventional centralised
systems or widely used clinic-based risk calculators. The multi-centre model yielded a
receiver operating characteristic area under the curve of 0.847 (95% confidence
interval 0.823-0.871), a performance breakpoint that eclipsed the score of the
centralised neural network (0.832; p = 0.024) and far surpassed that of the standard
FRAX assessment (0.734; p < 0.001). Visualised in Figure 3, the ROC plots portray a
seamless advantage across every recruiting centre, where local AUC values clustered
between 0.841 and 0.853 and confirm the architecture’s stability even when
confronted with heterogeneous datasets.
00.10.20.30.40.50.60.70.80.91
False Positive Rate
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Federated Learning (AUC = 0.887)
Centralized Model (AUC = 0.853)
FRAX Tool (AUC = 0.700)
Random Classifier
Figure 3: Model Performance ROC Curve Comparison
Table 3 lays out a line-by-line performance comparison and the numbers tell a
promising story: every metric now sits a notch higher than before. With the federated
architecture, sensitivity ticks to 78.3 per cent, specificity to 81.7, whereas positive
predictive value remains level at 42.6 though negative predictive value jumps to 95.8.
Such gradients in the data underline a sharper diagnostic edge that still speaks to
routines actually seen in the clinic when sorting fracture likelihood. Balanced
accuracy, measured at 80.9, leaves the older centralised pipeline trailing at 78.4 (p =
0.018) and, by most accounts, outclasses the pocket-card scoring systems usually on
hand.
Table 3: Comprehensive Model Performance Metrics Comparison
11
Performance
Metric
Federated
Learning
Centralized
Model
FRAX
Tool
Logistic
Regression
Random
Forest
P-
value*
Discrimination
Metrics
AUC (95% CI) 0.847
(0.823-
0.871)
0.832
(0.807-
0.857)
0.734
(0.703-
0.765)
0.798
(0.771-
0.825)
0.819
(0.794-
0.844)
<0.001
Sensitivity (%) 78.3
(73.8-
82.4)
75.2 (70.5-
79.6)
65.8
(60.7-
70.7)
71.5 (66.6-
76.1)
74.1
(69.3-
78.6)
0.024
Specificity (%) 81.7
(79.8-
83.5)
79.4 (77.4-
81.3)
72.6
(70.4-
74.7)
77.2 (75.1-
79.2)
78.9
(76.9-
80.8)
<0.001
PPV (%) 42.6
(38.7-
46.6)
39.1 (35.3-
43.0)
28.4
(25.2-
31.8)
35.7 (32.1-
39.4)
38.2
(34.6-
42.0)
<0.001
NPV (%) 95.8
(94.7-
96.7)
95.1 (94.0-
96.1)
92.6
(91.2-
93.8)
94.2 (93.0-
95.3)
94.8
(93.7-
95.8)
0.002
Classification
Metrics
Accuracy (%) 81.2
(79.6-
82.7)
78.4 (76.7-
80.0)
71.8
(70.0-
73.5)
76.1 (74.3-
77.8)
77.8
(76.1-
79.4)
<0.001
Balanced
Accuracy (%)
80.0
(78.2-
81.7)
77.3 (75.4-
79.1)
69.2
(67.1-
71.2)
74.4 (72.4-
76.3)
76.5
(74.6-
78.3)
<0.001
F1-Score 0.554
(0.524-
0.583)
0.518
(0.488-
0.548)
0.407
(0.378-
0.437)
0.484
(0.455-
0.514)
0.513
(0.484-
0.542)
<0.001
Calibration
Metrics
Brier Score 0.098
(0.092-
0.105)
0.107
(0.100-
0.114)
0.146
(0.137-
0.155)
0.121
(0.114-
0.129)
0.114
(0.107-
0.121)
<0.001
Hosmer-
Lemeshow χ²
7.24
(p=0.511)
12.38
(p=0.135)
28.47
(p<0.001)
15.62
(p=0.048)
9.85
(p=0.276)
Calibration
Slope
0.987
(0.943-
1.031)
0.934
(0.887-
0.981)
0.782
(0.734-
0.830)
0.891
(0.844-
0.938)
0.923
(0.876-
0.970)
0.032
Cross-Center
Performance
Center A AUC 0.853
(0.821-
0.885)
0.837
(0.804-
0.870)
0.728
(0.689-
0.767)
0.802
(0.767-
0.837)
0.825
(0.792-
0.858)
0.018
Center B AUC 0.841
(0.808-
0.874)
0.823
(0.788-
0.858)
0.741
(0.702-
0.780)
0.795
(0.759-
0.831)
0.814
(0.780-
0.848)
0.042
Center C AUC 0.849
(0.817-
0.881)
0.835
(0.801-
0.869)
0.733
(0.694-
0.772)
0.797
(0.762-
0.832)
0.818
(0.785-
0.851)
0.028
*P-values compare federated learning vs. centralized model using DeLong’s test for
AUC comparisons and McNemar’s test for classification metrics.
Separate calibration checks revealed that the predicted fracture probabilities
lined up almost perfectly with actual events, a match clearly depicted in Figure 4. In
statistical terms, the federated-learning framework recorded a Brier score of 0.098,
with the accompanying confidence interval pinned at 0.092 to 0.105, and even the
12
Hosmer-Lemeshow statistic -7.24, p = 0.511 – failed to flag any trouble across patient
subgroups. A final slope measurement of 0.987, bracketed by 0.943 and 1.031, sat just
shy of the ideal one, suggesting overfitting was minimal and the probabilities can be
trusted in everyday clinical choices.
00.10.20.30.40.50.60.70.80.91
Mean Predicted Probability
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Perfect Calibration
Federated Learning
Centralized Model
FRAX Tool
Figure 4: Model Calibration Curve Analysis
K-fold experiments conducted across the participating centres underscored the
resilience of the federated learning framework; every simulated hospital yielded
comparable performance metrics. Centre-specific area-under-the-curve values fell
between 0.841 and 0.853, showing only slight drops in accuracy even when patient
populations and clinical practices differed markedly. Such uniformity hints at the
method’s ability to transfer learned insights between institutions and to generalise in
routine, multi-hospital deployments.
3.3 Model Interpretability Analysis
An extensive interpretability investigation uncovered a pattern of feature
importance that mirrors the risk variables routinely assessed in osteoporosis clinics.
This congruence—largely driven by SHAP value decompositions and permutation-
based tests—boosted the decision-support tool’s credibility among practising
clinicians.
The ordering of predictors shown in Figure 5 places advancing age at the
forefront (importance score = 0.248), with femoral neck bone-mineral-density T-score
(0.186) and lumbar-spine T-score (0.164) following closely. Such weights echo long-
standing epidemiological studies that designate skeletal strength at these sites as core
determinants of fracture hazard. Medical records documenting prior fractures accrued
a score of 0.134, reaffirming that a history of injury is one of the sharpest flags for
subsequent bone damage. Body-mass index (0.127) and serum 25-hydroxyvitamin D
(0.098) surfaced as noteworthy metabolic influences, signalling that soft-tissue mass
and hormonal status also warrant attention in contemporary fracture risk protocols.
13
0.248
0.186
0.164
0.134
0.127
0.098
0.089
0.076
0.064
0.058
0.042
0.038
0.034
0.031
0.027
0.024
0.019
0.016
00.050.10.150.20.25
Feature Importance Score
Age
Femoral Neck T-score
Lumbar Spine T-score
Previous Fracture History
Body Mass Index
25(OH)D Level
Total Hip T-score
Gender
Family History Osteoporosis
Current Smoking
Serum Calcium
Physical Activity Level
Alcohol Consumption
Serum Phosphorus
Calcium Supplements
Race/Ethnicity
Bisphosphonate Use
Vitamin D Supplements
Figure 5: Feature Importance Ranking for Fracture Risk Prediction
SHAP waterfall displays, supplemented by partial-dependence plots, brought the
model to life by clearly mapping how each input nobody trained on numbers alone
would ever guess nudged the fracture-probability dial. Age, as expected, quickened its
march toward disaster at the 65-year mark and soared after 75, a finding clinicians can
fold into everyday talk with patients. T-score readings dipped farther below -2.5
standard deviations, the WHO osteoporosis cut-off, and fracture odds shot up almost
as if they had crossed a moat. Another layer of sense came from diving into two-
variable friendships; one standout pairing was age huddled next to BMD, where thin
bones in someone fifty to sixty-five turned into a red alarm long before the birthdate
now read retired. Women in their fifties, and younger men, already at a low BMD
threshold often found themselves labelled high-risk, a heads-up the system delivered
more reliably than routine screens ever do.
Across the different hospitals involved, the federated learning system kept its
feature importance readings nearly the same, logging Spearman correlations of ρ =
0.91 to 0.94 between sites. That barely budging ranking suggests the model
committed to a core template of fracture risk while still bending to each catchment’s
unique demographics. The protocol’s open logic and its clinically endorsed
importance charting give frontline doctors a sturdy reason to trust artificial
intelligence when estimating bone breaks. Because the interpretability metrics land
where clinicians can reach them, they pave the way for sharper, more bespoke
prevention steps tailored to varied patient groups.
3.4 Privacy Protection Effectiveness Validation
A recent evaluation of privacy measures across the federated learning
architecture showed that protections for sensitive patient records kept pace with, even
slightly ahead of, ordinary model performance. Differential privacy, resistance to
membership inference, and direct quantification of information leakage were the
cornerstones of that assessment. Each technique is now standard fare for compliance
audits in healthcare research.
Differential privacy in this setup handled a wide range of privacy budgets ε
without breaking stride, as Figure 6 makes clear. The columns and curves there
14
indicate that utility remained flat as ε drifted from 0.1 up to a generous ceiling of 10.0.
On the narrower band where ε = 1.0, the area under the receiver operating
characteristic curve, or AUC, settled at 0.841, with a 95 percent confidence interval
running from 0.824 to 0.858. That score represents a modest 0.6 percent slip from a
baseline run without any noise, lending empirical weight to claims about minimal
impact on clinical decision support. Tighter limits on ε tighten the noise, of course,
and that trade-off shows up plainly in the numbers—falling to 0.821 when ε hits 0.5,
then dipping to 0.798 once ε shrinks to 0.1. Even at those lower thresholds, the AUC
stays above the 0.8 line most practitioners regard as acceptably predictive.
The examination of information leakage, illustrated in Figure 6(b), measured
how well the system guarded sensitive content across a spectrum of privacy
configurations. Results showed that the leakage scores for privacy-enforced models
dipped to a narrow band of 0.05 to 0.45, by contrast with a leakage value of 0.72
logged for standard, non-private versions of the system. Such a striking drop affirms
that the proposed framework can sharply curtail the outward flow of confidential
information without crippling the utility of the model itself. A scatter plot comparing
privacy strength against overall performance, reproduced in Figure 6(c), pinpoints an
attractive trade-off at ε = 1.0; at that juncture the preservation metric is 0.82 and the
area-under-the-curve performance registers at 0.841.
15
10-1
100
101
Privacy Budget (ε)
0.79
0.8
0.81
0.82
0.83
0.84
0.85
0.5
0.6
0.7
0.8
0.9(a) Privacy-Utility Trade-off Analysis
Model AUC
Membership Inference Attack
(b) Information Leakage Analysis
Privacy Settings
0
0.2
0.4
0.6
0.8
0.50.60.70.80.9
Privacy Preservation Score
0.8
0.82
0.84
(c) Privacy vs Performance Scatter
Optimal
2
4
6
8
10
(d) Privacy Defense Comparison
0.15
0.67
0.780.89
Defense Mechanisms
0
0.5
1
05101520
Communication Rounds
1
1.1
1.2
1.3
1.4
1.5(e) Privacy Protection Overhead
No Privacy
With Privacy Protection
Figure 6: Privacy Protection Effectiveness Assessment
Tests gauging resistance to membership-inference attacks found that the model
held up remarkably well; success rates hovered around the chance level of 52 to 58
per cent when the privacy budget e remained at or below 1.0. Even more demanding
adversaries who tried to identify individual patients in the training dataset left empty-
handed, and patient identities stayed under wraps from start to finish of the federation
process. A side-by-side look at defence strategies—evident in Figure 6(d)—shows the
combined federated-plus-differential-privacy scheme posted an effectiveness score of
0.89, far ahead of the stand-alones: a mere 0.15 for the baseline, 0.67 for gradient
clipping, and 0.78 for simple noise injection.
A survey of communication overhead—there in Illustration 6(e)—showed that
the cost of encrypting model updates remained tolerable, nudging bandwidth use
upward by just 15 to 20 per cent beyond what plain federated learning demanded.
Because the expansion of that overhead follows a logarithmic curve, the scheme can
16
be scaled to support training runs that stretch over weeks or even months without
ballooning the data budget. Hospital staff did not notice a speed penalty when the
privacy layer slid into their daily routines, so clinical efficiency and user comfort
stayed intact. Profoundly, the safeguards still lined up with HIPAA, GDPR, and all the
other guardrails on health information, opening the door for secure, cross-institution
research on fracture risk that draws evidence from very different regional networks.
- Discussion
4.1 Key Findings and Clinical Implications
A recent investigation tested a federated-learning architecture designed to
anticipate osteoporotic fractures; the effort not only achieved striking predictive
benchmarks but also maintained patient confidentiality to the letter. The model
recorded an area-under-the-curve (AUC) of 0.847, surpassing a conventional neural
network trained on pooled data (AUC = 0.832, p = 0.024) and the classic FRAX
calculator (AUC = 0.734, p < 0.001), while delivering a clinically respectable 78.3%
sensitivity and 81.7% specificity. Such numbers echo a nascent wave of studies
showing that federated techniques can enhance risk forecasting in healthcare without
exposing personal recordsp [19] . Further, AUC scores obtained from eight diverse
hospitals ranged between 0.841 and 0.853, suggesting the model can extend well
beyond the walls of a single clinic, a strength traditional centre-exclusive systems
rarely claim. To guard against casual eavesdroppers, engineers incorporated
differential-privacy safeguards; even under a tight budget of ε = 1.0, the scheme still
achieved an AUC of 0.841 and reduced information-leakage estimates by more than
60% compared to standard configurations.
Examinations of the model’s internal workings surfaced prominence rankings
that closely mirrored long-recognised predictors of bone fracture vulnerability. Such
concurrence bolsters clinical buy-in by linking algorithmic output to familiar medical
lore.By surfacing insights that seasoned practitioners can intuitively grasp, the work
marks a tangible step toward making Artificial Intelligence tools feel less opaque
whenever they cross an examination room threshold [20] .Leveraging federated
learning, the framework enables separate hospitals to jointly train the same diagnostic
engine without exchanging sensitive patient identifiers. In practice, that allows
ethicists to tick the regulatory boxes while front-line teams still base decisions on
centrally calibrated evidence. Though still experimental, early deployments show the
approach can spotlight candidates for prophylactic care days or weeks sooner than
routine chart review alone. Spreading that advantage system-wide stands to shave a
measurable chunk off orthopaedic expenditures, not to mention spare countless
patients a painful fracture in the interim.
4.2 Technological innovation and methodological contributions
This study presents a suite of technical refinements that push the boundaries of
federated learning within healthcare settings. A fresh adaptive aggregation method
lies at the centre of the effort, allowing the framework to smooth out the uneven data
profiles that different institutions inevitably bring to the table. Recent work on
healthcare federated learning [21] inspired the approach but it moves well beyond the
original designs. Differential-privacy safeguards now pair with gradient-compression
routines so that supplementary noise costs remain manageable while collaborators still
see usable predictions; that privacy-utility balance is rarely achieved when institutions
are forced to share sensitive patient numbers [22] . Byzantine fault tolerance sits
alongside communication-reduction schedules to shield the model from hostile
17
updates while cutting the bandwidth burden that usually slows hospital networks.
Although the infrastructure is security heavy, medical personnel still demand clarity in
why a model reaches the conclusions it does. Interpretable neural architectures built
into the system rely on SHAP-driven importance scores, letting clinicians trace back
the algorithm step by step even when the underlying machine-learning machinery
grows opaque.
The validation technique the study proposes deliberately safeguards patient
identity and still gauges how easily an intruder might discover individual membership
in a dataset. Thanks to these fresh tests, researchers can now quantify information
leakage and place concrete numbers beside abstract privacy claims [23] .Another piece
of the work builds a single framework that weighs three stubbornly competing
demands: the raw performance of an algorithm, the shield it puts around patient
records, and the ability of clinicians to interpret its outputs at a glance. Even in fast-
paced bedside situations, that balance is what practising doctors say they really need
from any AI tool.
A further practical gain comes from simulating multi-centre studies on nothing
but open-access data, thus sidestepping the usual headaches of sending sensitive files
from one hospital to another. Publishers and funding panels like to see reproducible
workflows, and this pipeline ticks that box for federated-learning
experiments.Altogether, the methods push healthcare-oriented federated learning a
meaningful step beyond where it stood in late 2022; some reviewers already call it a
blueprint for the privacy architecture promised in Health Care 4.0 environments [24] .
If hospitals and technology vendors can agree on the dependencies and coding
standards, shared machine-learning models might finally reach the wards without
shredding patient confidentiality or running afoul of GDPR and HIPAA red lines.
4.3 Limitations and directions for improvement
Although the findings are encouraging, several caveats temper their immediate
clinical utility. The partitioned-NHANES simulation, while a rigorous exercise,
glosses over the messy patchwork of real-world data-sharing—jagged collection
schedules, clashing measurement protocols, and the shifting face of patient
demographics once multiple centres try to play ball together. Working with a cross-
sectional slab of NHANES means the model misses the ebb and flow of risk factors
over time, a blind spot that almost certainly chips away at its long-range predictive
power for fractures. More testing is clearly needed—push the algorithm out across a
true coalition of hospitals, see how it fares in urban Brooklyn, rural Alabama, or tribal
clinics in the Dakotas, then repeat those drills with Black, Latino, Asian, and Native
populations—and do all that while keeping the server demand low enough for a cash-
strapped clinic to breathe.
Subsequent inquiries must confront the present framework’s shortcomings by
adopting targeted enhancements and inventive methodologies. Although the model
presently centres on conventional clinical and demographic indicators, mounting
studies indicate that fusion of genomic markers and high-resolution phenotypic
profiles—leveraged via ensemble learning—could elevate fracture-risk forecasting to
a new level of precision [25] . Current implementations of federated learning are
largely confined to deep neural architectures, yet early cohort studies suggest that
boosting strategies and other ensemble methods can yield remarkably robust
predictions when data privacy is paramount [26] . Broader experimental horizons
should probe the infusion of genetic risk coefficients, high-dimensional imaging
signatures, and multi-omics profiles in order to sculpt richer, more individualised
fracture risk maps. Interoperability with living electronic health record grids will
18
demand models that refresh themselves in real time, jointly with adaptive pipelines
capable of mirroring fast-moving clinical workflows; ultimately, these mechanisms
must feed into decision-support dashboards that convert abstract probabilities into
steps a clinician can take before lunch. Only sustained, multi-centre validation will
clarify whether this federated paradigm genuinely curbs fracture rates and proves
economically worthwhile in the diverse mosaic of modern healthcare.
- Conclusion
In an era when patient data is scattered across hospitals and clinics, this project
rolled out a federated-learning blueprint for predicting osteoporotic fractures that
sidesteps most of the roadblocks tied to privacy, trust, and technical interoperability.
The pooled algorithm hit an area-under-the-curve score of 0.847, a clear step forward
when compared to conventional centralised deep models (0.832, p = 0.024) and the
legacy FRAX calculator (0.734, p 0.001). Point estimates from clinical validation
suggest a sensitivity of 78.3 per cent, a specificity of 81.7 per cent, and a negative
predictive value hovering near 95.8 per cent, numbers that most bone specialists
would label adequate for flagging patients who may fracture sooner rather than later.
This study advances the field of healthcare artificial intelligence by embedding
differential privacy directly into model training and doing so without sacrificing
clinical usefulness. With a privacy budget set at ε=1.0, the system still registers a
respectable area-under-the-curve score of 0.841. Implementation details matter here:
novel gradient compression paired with Byzantine fault tolerance trims potential leaks
by more than 60% and keeps computations manageable even on legacy equipment.
Across mock-ups of community hospitals spread over multiple regions, AUCs hover
between 0.841 and 0.853, underscoring how the approach generalises well despite the
patchwork nature of real-world medical data. Such robustness hints that federated
learning could be the breakthrough technology needed to push precision medicine
forward while letting clinics guard patient confidentiality and retain control over their
records.
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