LGOct 26, 2015
Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks
arXiv:1510.07641v239 citations
Originality Synthesis-oriented
AI Analysis
This addresses the problem of automated diagnosis classification from clinical data for healthcare applications, but it is incremental as it applies an existing method to a new domain.
The paper tackled multilabel classification of diagnoses from variable-length clinical time series using LSTM recurrent neural networks, achieving performance that outperformed a strong baseline on various metrics.
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.