When looking at the performance, it doesn't seem like the LSTM adds very much compared to simpler multi-layer perceptrons. I wonder if that is because temporal information isn't very useful or because the LSTM wasn't capable of learning some of the more complicated temporal patterns.
I would love to play around with the data, unfortunately, I assume it's private (for good reason) due to potentially sensitive patient data.
If anyone is interested in related methods for time series, I just put up code for our recent action segmentation paper using Temporal Convolutional Networks (https://github.com/colincsl/TemporalConvolutionalNetworks). I would be very interested in trying this model on the ICU data. In my experience, TCNs are much better at learning complex temporal pattern than LSTMs.
I would love to play around with the data, unfortunately, I assume it's private (for good reason) due to potentially sensitive patient data.
If anyone is interested in related methods for time series, I just put up code for our recent action segmentation paper using Temporal Convolutional Networks (https://github.com/colincsl/TemporalConvolutionalNetworks). I would be very interested in trying this model on the ICU data. In my experience, TCNs are much better at learning complex temporal pattern than LSTMs.