Erakusten 1 - 9 emaitzak -- 9 bilaketa honetara 'Shingo Iwami (266092)', 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)

    Argitaratua 2025
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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)

    Argitaratua 2025
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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)

    Argitaratua 2025
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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)

    Argitaratua 2025
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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)

    Argitaratua 2025
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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)

    Argitaratua 2025
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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
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