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Fig 1.

Schematic of machine learning benchmarking study design.

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Table 1.

Baseline characteristics of heart transplant recipients from October 18, 2018 to June 3, 2021.

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Table 1 Expand

Fig 2.

Violin plots comparing cross-validation C-indices for 7 different machine learning survival models against Cox PH in the A) post-policy cohort and B) pre-policy cohort.

Black bar indicates the mean c-index.

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Fig 2 Expand

Table 2.

C-indices for holdout data.

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Table 2 Expand

Fig 3.

Average number of times variable is selected across all sparse machine learning models in the A) post-policy cohort and B) pre-policy cohort.

Sparse machine learning models include Lasso, Elastic Net, and Cox Boost.

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Fig 3 Expand

Fig 4.

Top 20 significant predictor variables.

A) RSF in post-policy cohort B) RSF in pre-policy cohort C) Cox Boost in post-policy cohort D) Cox Boost in pre-policy cohort E) XGBT in post-policy cohort F) XGBT in pre-policy cohort.

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Fig 4 Expand