Mostra 1 - 9 risultati di 9 ricerca 'Naotoshi Nakamura (12083048)', tempo di risposta: 0,01s Raffina i risultati
  1. 1
    Comparison of prediction performance with previously reported metrics.

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

    Pubblicazione 2025
    Altri autori: “...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... di Raiki Yoshimura (22676573)

    Pubblicazione 2025
    Altri autori: “...Naotoshi Nakamura (12083048)...”
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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. di Raiki Yoshimura (22676573)

    Pubblicazione 2025
    Altri autori: “...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. di Raiki Yoshimura (22676573)

    Pubblicazione 2025
    Altri autori: “...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. di Raiki Yoshimura (22676573)

    Pubblicazione 2025
    Altri autori: “...Naotoshi Nakamura (12083048)...”
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  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. di Raiki Yoshimura (22676573)

    Pubblicazione 2025
    Altri autori: “...Naotoshi Nakamura (12083048)...”
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  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. di Raiki Yoshimura (22676573)

    Pubblicazione 2025
    Altri autori: “...Naotoshi Nakamura (12083048)...”
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  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. di Raiki Yoshimura (22676573)

    Pubblicazione 2025
    Altri autori: “...Naotoshi Nakamura (12083048)...”
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  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. di Raiki Yoshimura (22676573)

    Pubblicazione 2025
    Altri autori: “...Naotoshi Nakamura (12083048)...”
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