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A conditional survival distribution-based method for censored data imputation: overcoming the hurdle in machine learning-based survival analysis
ABSTRACT Data analyses by machine learning (ML) algorithms are gaining popularity in biomedical research. When time-to-event data are of interest, censoring is common and needs to be properly addressed. Most ML methods cannot conveniently and appropriately take the censoring information into consideration, potentially leading to inaccurate or biased results. We …