Erakusten 1 - 9 emaitzak -- 9 bilaketa honetara 'Naotoshi Nakamura (12083048)', Bilaketaren denbora: 0,01s Findu emaitzak
  1. 1
    Comparison of prediction performance with previously reported metrics.

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

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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... nork Raiki Yoshimura (22676573)

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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. nork Raiki Yoshimura (22676573)

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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. nork Raiki Yoshimura (22676573)

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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. nork Raiki Yoshimura (22676573)

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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. nork Raiki Yoshimura (22676573)

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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. nork Raiki Yoshimura (22676573)

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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. nork Raiki Yoshimura (22676573)

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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. nork Raiki Yoshimura (22676573)

    Argitaratua 2025
    Beste egile batzuk: “...Naotoshi Nakamura (12083048)...”
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