Εμφανίζονται 1 - 9 Αποτελέσματα από 9 για την αναζήτηση 'Naotoshi Nakamura (12083048)', χρόνος αναζήτησης: 0,01δλ Περιορισμός αποτελεσμάτων
  1. 1
    Comparison of prediction performance with previously reported metrics.

    Comparison of prediction performance with previously reported metrics. ανά Raiki Yoshimura (22676573)

    Έκδοση 2025
    Άλλοι συγγραφείς: “…Naotoshi Nakamura (12083048)…”
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  2. 2
    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.

    Stratification and characterization of groups showing different graft survival: (A) UMAP visualization of the stratified derivation cohort data based on the distance matrix from th... ανά Raiki Yoshimura (22676573)

    Έκδοση 2025
    Άλλοι συγγραφείς: “…Naotoshi Nakamura (12083048)…”
    Προσθήκη στα αγαπημένα
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  3. 3
    Comparison of performance of four machine learning models for early graft loss prediction.

    Comparison of performance of four machine learning models for early graft loss prediction. ανά Raiki Yoshimura (22676573)

    Έκδοση 2025
    Άλλοι συγγραφείς: “…Naotoshi Nakamura (12083048)…”
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  4. 4
    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. ανά Raiki Yoshimura (22676573)

    Έκδοση 2025
    Άλλοι συγγραφείς: “…Naotoshi Nakamura (12083048)…”
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  5. 5
    Predicting stratified groups in the validation cohort: (A) Workflows for the multi-class classification and hierarchical classification models are illustrated.

    Predicting stratified groups in the validation cohort: (A) Workflows for the multi-class classification and hierarchical classification models are illustrated. ανά Raiki Yoshimura (22676573)

    Έκδοση 2025
    Άλλοι συγγραφείς: “…Naotoshi Nakamura (12083048)…”
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    Αποθηκεύτηκε σε:
  6. 6
    Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups.

    Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups. ανά Raiki Yoshimura (22676573)

    Έκδοση 2025
    Άλλοι συγγραφείς: “…Naotoshi Nakamura (12083048)…”
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    Αποθηκεύτηκε σε:
  7. 7
    Comparison of hierarchical classifier performance among four machine learning models for the early-loss, intermediate-loss, and late/no-loss groups.

    Comparison of hierarchical classifier performance among four machine learning models for the early-loss, intermediate-loss, and late/no-loss groups. ανά Raiki Yoshimura (22676573)

    Έκδοση 2025
    Άλλοι συγγραφείς: “…Naotoshi Nakamura (12083048)…”
    Προσθήκη στα αγαπημένα
    Αποθηκεύτηκε σε:
  8. 8
    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.

    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. ανά Raiki Yoshimura (22676573)

    Έκδοση 2025
    Άλλοι συγγραφείς: “…Naotoshi Nakamura (12083048)…”
    Προσθήκη στα αγαπημένα
    Αποθηκεύτηκε σε:
  9. 9
    Comparison of multi-class classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups.

    Comparison of multi-class classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups. ανά Raiki Yoshimura (22676573)

    Έκδοση 2025
    Άλλοι συγγραφείς: “…Naotoshi Nakamura (12083048)…”
    Προσθήκη στα αγαπημένα
    Αποθηκεύτηκε σε:

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