Dynamic Outcomes-Based Clustering of Disease Trajectory in Mechanically Ventilated Patients

We trained different time series models to embed medical time series data from mechanical ventilation episodes, and then we clustered these to uncover hidden patient subtypes in the data.

Predicting Patient Outcomes with Graph Representation Learning

Our model, LSTM-GNN, is designed to take advantage of similarity between patients in the EHR (established using the diagnoses). First, it processes the time series data for each patient with the LSTM component, before sharing information within the neighbourhood of patients via the GNN. This is an alternative way of presenting diagnoses information (the common approach is to use an encoder in the late stages of a model). We found that using both methods together gains the best performance.