Totally new to LSTMs and would like some guidance on how to structure input data for classification using multivariate longitudinal data. Most, if not all, tutorials online are non-healthcare related and I could not find a good analogy as an example to work from.
The problem: predicting that a hospitalization event will occur in the next 30-90 days (where days 0-29 is a lag period for intervention).
The data: Each observation is one patient, and each patient has several lab outcomes with many sequential values. Each patient also has non-sequential data, like gender and race.
Questions: 1. How do I specify the individual values of one lab outcome's sequence from another's? 2. How do I input non-sequential variables along with sequential? 3. How should LSTM parameters/architecture be adjusted to accommodate?
My thinking so far is that the data should be structured using an array, where each row is the sequence of a lab outcome, and non-sequential attributes exist as sequences with the same value in each column. I assume that the LSTM inherently knows that each row is another variable.
Here are two papers that are helpful for context: 1. Learning to diagnose with LSTM RNNs https://arxiv.org/abs/1511.03677v7 2. Multi task prediction of disease onset from longitudinal lab tests https://arxiv.org/abs/1608.00647v3
I am using python: keras with tensorflow.