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.

Temporal Pointwise Convolutional Networks for Length of Stay Prediction in the Intensive Care Unit

Our model, Temporal Pointwise Convolution (TPC), is specifically designed to mitigate common challenges with Electronic Health Records, such as skewness, irregular sampling and missing data. We have achieved significant performance benefits of 18-68% over the Long-Short Term Memory (LSTM) network, and the Transformer.

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.

Deep Transfer Learning for Automated Diagnosis of Skin Lesions from Photographs

Transfer learning with convolutional neural networks on the task of melanoma classification.