Raw performance metrics for the MLP model.

Raw performance metrics for the MLP model.

<p>The table presents the raw output from the bootstrap analysis, showing the mean, lower, and upper confidence interval bounds for the AUC of local and federated models, and the performance gain (<i>Δ</i>AUC). It provides a hospital-level breakdown of the mean AUC and 95% CI for t...

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Détails bibliographiques
Auteur principal: Roberta Moreira Wichmann (14259316) (author)
Autres auteurs: Murilo Afonso Robiati Bigoto (22676715) (author), Alexandre Dias Porto Chiavegatto Filho (14259328) (author)
Publié: 2025
Sujets:
Cell Biology
Ecology
Cancer
Science Policy
Biological Sciences not elsewhere classified
Chemical Sciences not elsewhere classified
Information Systems not elsewhere classified
via parameter aggregation
using ensemble aggregation
strong inverse correlation
maximum performance degradation
benefits requires caution
beneficial collaborative effect
>&# 916 ;</
19 mortality using
19 mortality prediction
local patient volume
statistically certain benefits
statistical significance showed
0273 ]) due
auc crossed zero
study validates fl
fl models demonstrated
performance gain (<
evaluated federated learning
div >< p
collaborative model ’
auc minus auc
local data scarcity
ci [– 0
gain ’
federated learning
local validation
universally certain
statistical advantage
6307 ])
auc across
– 0
volatile algorithm
smallest hospital
particularly evident
network (<
multicentric sample
multicenter sample
limited institutions
layer perceptron
iid ).
high sensitivity
findings underscore
every site
enabling mechanism
despite achieving
confidence intervals
500 ).
022 patients
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