A Machine Learning Model for Predicting Mortality within 90 Days of Dialysis Initiation.

Background: The first 90 days after dialysis initiation are associated with high morbidity and mortality in end-stage kidney disease (ESKD) patients. A machine learning-based tool for predicting mortality could inform patient-clinician shared decision making on whether to initiate dialysis or pursue medical management. We used the eXtreme Gradient Boosting (XGBoost) algorithm to predict mortality in the first 90 days after dialysis initiation in a nationally representative population from the United States Renal Data System.

DASH, the data and specimen hub of the National Institute of Child Health and Human Development.

The benefits of data sharing are well-established and an increasing number of policies require that data be shared upon publication of the main study findings. As data sharing becomes the new norm, there is a heightened need for additional resources to drive efficient data reuse. This article describes the development and implementation of the Data and Specimen Hub (DASH) by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) to promote data sharing from NICHD-funded studies and enable researchers to comply with NIH data sharing policies.