Stanford researchers have developed a method for assessing neonatal health risk by using longitudinal electronic health records (EHR) utilizing a machine learning model comprising deep learning neural networks.
GateFinder is a flexible, automated, objective algorithm that quickly analyzes complex mass cytometry datasets to identify simple signatures (“gates”) for FACS (fluorescent automated cell sorting) purification.