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Comparison of performance of four machine learning models for long-term graft loss prediction.

Comparison of performance of four machine learning models for long-term graft loss prediction.

<p>Comparison of performance of four machine learning models for long-term graft loss prediction.</p>

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Bibliografske podrobnosti
Glavni avtor: Raiki Yoshimura (22676573) (author)
Drugi avtorji: Naotoshi Nakamura (12083048) (author), Takeru Matsuura (22676576) (author), Takeo Toshima (13988776) (author), Takasuke Fukuhara (673475) (author), Kazuyuki Aihara (44090) (author), Katsuhito Fujiu (559488) (author), Shingo Iwami (266092) (author), Tomoharu Yoshizumi (2621434) (author)
Izdano: 2025
Teme:
Medicine
urgent need exists
stage liver disease
shorter waiting times
recent years thanks
populations using data
model enabled us
highly heterogeneous sample
appropriate patient care
30 days postoperatively
distinct population similar
different survival times
hierarchical prediction method
better graft quality
survival time
loss population
graft loss
better performance
unexpected infections
transplanted organ
three groups
several models
next categorized
mediated rejection
loss groups
loss group
living donors
gained importance
five groups
conventional models
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  • Prediction of early graft loss at the individual level and identification of crucial factors: (A) Survival curves for all patients who experienced graft loss are plotted.
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  • Stratification and characterization of groups showing different graft survival: (A) UMAP visualization of the stratified derivation cohort data based on the distance matrix from the RF clustering.
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  • Comparison of multi-class classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups.
    od: Raiki Yoshimura (22676573)
    Izdano: (2025)

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