Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks

Understand

Deep neural networks have shown promising results for various clinical prediction tasks such as diagnosis, mortality prediction, predicting duration of stay in hospital, etc.

• However, training deep networks -- such as those based on Recurrent Neural Networks (RNNs) -- requires large labeled data, high computational resources, and significant hyperparameter tuning effort.
• In this work, we investigate as to what extent can transfer learning address these issues when using deep RNNs to model multivariate clinical time series.
• We consider transferring the knowledge captured in an RNN trained on several source tasks simultaneously using a large labeled dataset to build the model for a target task with limited labeled data.