Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks
By Zachary Chase Lipton, David C. Kale, Randall C. Wetzel. 2015
39 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Signal Processing
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In Artificial Intelligence
Citations in the corpus
- Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence (2019, methodology): “We used a default of 128 hidden units per layer as reported in multiple publications 21,22 and two layers of LSTM cells as suggested by the commonly adopted bidirectional LSTM; 23 we used a default learning rate of 0.001 from Tensorflow.”
- Explainable machine-learning predictions for the prevention of hypoxaemia during surgery (2018, unknown): “12).”