Multilabel time series classification with LSTM | Hacker News Reader

Multilabel time series classification with LSTM

github.com

81 pointsaqibsaeed9 comments

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.

That's true, according to the paper improvement over MLP is negligible. It looks like the model is very small and that might be a reason for relatively low performance.

The performance of multilayer perceptrons and linear models is significant. The paper additionally allows the MLP and linear models to "cheat" by giving them heavily hand-engineered features incorporating extensive prior knowledge. Even with this benefit, the LSTM outperforms the baselines, but the improvement is considerably smaller.

The performance improvement over

Not OP, but for reader convenience, the paper linked to by the github repo is entitled "Learning to Diagnose with LSTM Recurrent Neural Networks"

And the abstract is: "Clinical medical data, especially in the intensive care unit (ICU), consist of multivariate time series of observations. For each patient visit (or episode), sensor data and lab test results are recorded in the patient's Electronic Health Record (EHR). While potentially containing a wealth of insights, the data is difficult to mine effectively, owing to varying length, irregular sampling and missing data. Recurrent Neural Networks (RNNs), particularly those using Long Short-Term Memory (LSTM) hidden units, are powerful and increasingly popular models for learning from sequence data. They effectively model varying length sequences and capture long range dependencies. We present the first study to empirically evaluate the ability of LSTMs to recognize patterns in multivariate time series of clinical measurements. Specifically, we consider multilabel classification of diagnoses, training a model to classify 128 diagnoses given 13 frequently but irregularly sampled clinical measurements. First, we establish the effectiveness of a simple LSTM network for modeling clinical data. Then we demonstrate a straightforward and effective training strategy in which we replicate targets at each sequence step. Trained only on raw time series, our models outperform several strong baselines, including a multilayer perceptron trained on hand-engineered features."

Here is an online ipynb viewer to view the code[0].

[0] - http://nbviewer.jupyter.org/github/aqibsaeed/Multilabel-time...

What is the format of data.csv? An example would be easier to understand the inputs.

Check this data set (http://mimic.mit.edu/about/mimic/) and extract relevant features as discussed in the paper.

what is the format of data.csv

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