RIDDLE
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About
RIDDLE (Race and ethnicity Imputation from Disease history with Deep LEarning) is an open-source Python2 library for using deep learning to impute race and ethnicity information in anonymized electronic medical records (EMRs). RIDDLE provides the ability to (1) build models for estimating race and ethnicity from clinical features, and (2) interpret trained models to describe how specific features contribute to predictions. The RIDDLE library implements the methods introduced in “RIDDLE: Race and ethnicity Imputation from Disease history with Deep LEarning” (PLOS Computational Biology, 2018). RIDDLE is designed to handle large and high-dimensional datasets in a performant fashion. RIDDLE trains models efficiently by using a parallelized TensorFlow-under-Keras backend, and avoids memory overflow by preprocessing data in conjunction with batch-wise training.
Platform
Task
Features
• train models efficiently using a parallelized tensorflow-under-keras backend
• handle large and high-dimensional datasets
• interpret trained models to describe how specific features contribute to predictions
• build models for estimating race and ethnicity from clinical features
• compute deeplift feature contribution scores
• avoid memory overflow by preprocessing data in conjunction with batch-wise training
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