Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks

Abstract

We present a novel application of LSTM recurrent neural networks to multilabel classification of diagnoses given variable-length time series of clinical measurements. Our method outperforms a strong baseline on a variety of metrics.

References in corpus (2)

• Sequence to Sequence Learning with Neural Networks
• A Critical Review of Recurrent Neural Networks for Sequence Learning

Cited by in corpus (4)

• RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records
• Machine Learning and Visualization in Clinical Decision Support: Current State and Future Directions
• Medical Diagnosis From Laboratory Tests by Combining Generative and Discriminative Learning
• Unsupervised Learning for Computational Phenotyping