Extracting Patterns using Neural Networks

Question

I am trying to extract common patterns that always appear whenever a certain event occurs.

For example, patient A, B, and C all had a heart attack. Using the readings from there pulse, I want to find the common patterns before the heart attack stroke.

In the next stage I want to do this using multiple dimensions. For example, using the readings from the patients pulse, temperature, and blood pressure, what are the common patterns that occurred in the three dimensions taking into consideration the time and order between each dimension.

What is the best way to solve this problem using Neural Networks and which type of network is best? (Just need some pointing in the right direction)

and thank you all for reading

Answer 1

Described problem looks like a time series prediction problem. That means a basic prediction problem for a continuous or discrete phenomena generated by some existing process. As a raw data for this problem we will have a sequence of samples x(t), x(t+1), x(t+2), ..., where x() means an output of considered process and t means some arbitrary timepoint.

For artificial neural networks solution we will consider a time series prediction, where we will organize our raw data to a new sequences. As you should know, we consider X as a matrix of input vectors that will be used in ANN learning. For time series prediction we will construct a new collection on following schema.

In the most basic form your input vector x will be a sequence of samples (x(t-k), x(t-k+1), ..., x(t-1), x(t)) taken at some arbitrary timepoint t, appended to it predecessor samples from timepoints t-k, t-k+1, ..., t-1. You should generate every example for every possible timepoint t like this.

But the key is to preprocess data so that we get the best prediction results.

Assuming your data (phenomena) is continuous, you should consider to apply some sampling technique. You could start with an experiment for some naive sampling period Δt, but there are stronger methods. See for example Nyquist–Shannon Sampling Theorem, where the key idea is to allow to recover continuous x(t) from discrete x(Δt) samples. This is reasonable when we consider that we probably expect our ANNs to do this.

Assuming your data is discrete... you still should need to try sampling, as this will speed up your computations and might possibly provide better generalization. But the key advice is: do experiments! as the best architecture depends on data and also will require to preprocess them correctly.

The next thing is network output layer. From your question, it appears that this will be a binary class prediction. But maybe a wider prediction vector is worth considering? How about to predict the future of considered samples, that is x(t+1), x(t+2) and experiment with different horizons (length of the future)?

Further reading:

1. Somebody mentioned Python here. Here is some good tutorial on timeseries prediction with Keras: Victor Schmidt, Keras recurrent tutorial, Deep Learning Tutorials
2. This paper is good if you need some real example: Fessant, Francoise, Samy Bengio, and Daniel Collobert. "On the prediction of solar activity using different neural network models." Annales Geophysicae. Vol. 14. No. 1. 1996.