Transfer learning from PREDICT v3 and de-novo random survival forests improve calibration of five-year breast cancer survival predictions over the baseline in MA.27 data while handling missing information, with benefits seen in SEER but not TEAM validation.
Insights into the performance of PREDICT tool in a large Mainland Chinese breast cancer cohort: a comparative analysis of versions 3.0 and 2.2
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Transfer Learning and Machine Learning for Training Five Year Survival Prognostic Models in Early Breast Cancer
Transfer learning from PREDICT v3 and de-novo random survival forests improve calibration of five-year breast cancer survival predictions over the baseline in MA.27 data while handling missing information, with benefits seen in SEER but not TEAM validation.