A mostrar 1 - 9 resultados de 9 para a pesquisa 'Naotoshi Nakamura (12083048)', tempo de pesquisa: 0.01seg Refinar resultados
  1. 1
    Comparison of prediction performance with previously reported metrics.

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

    Publicado em 2025
    Outros Autores: “...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... Por Raiki Yoshimura (22676573)

    Publicado em 2025
    Outros Autores: “...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. Por Raiki Yoshimura (22676573)

    Publicado em 2025
    Outros Autores: “...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. Por Raiki Yoshimura (22676573)

    Publicado em 2025
    Outros Autores: “...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. Por Raiki Yoshimura (22676573)

    Publicado em 2025
    Outros Autores: “...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. Por Raiki Yoshimura (22676573)

    Publicado em 2025
    Outros Autores: “...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. Por Raiki Yoshimura (22676573)

    Publicado em 2025
    Outros Autores: “...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. Por Raiki Yoshimura (22676573)

    Publicado em 2025
    Outros Autores: “...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. Por Raiki Yoshimura (22676573)

    Publicado em 2025
    Outros Autores: “...Naotoshi Nakamura (12083048)...”
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