Patient Clustering and Classification for Vital Organ Failure Using ICD Code with Graph Attention

Abstract

AbstractObjectiveHeart failure, respiratory failure and kidney failure are three severe organ failures (OF) that have high mortalities and are most prevalent in intensive care units. The objective of this work is to offer insights on OF clustering from the aspects of graph neural network and diagnosis history.MethodsThis paper proposes a neural network-based pipeline to cluster three types of organ failure patients by incorporating embedding pre-train using an ontology graph of International Classification of Diseases (ICD) codes. We employ an autoencoder-based deep clustering architecture jointly trained with a K-means loss, and a non-linear dimension reduction is performed to obtain patient clusters on the MIMIC-III dataset.ResultsThe clustering pipeline shows superior performance on a public-domain image dataset. For MIMIC-III, the model gives two distinct clusters that are related to the severity of the diseases. The learnt ICD embeddings present strong power in identifying the OF type in supervised learning.ConclusionOur proposed pipeline gives stable clusters, however, they do not correspond to the type of OF which indicates these OF share significant hidden characteristics in diagnosis. These clusters can be used to signal possible complications and severity of illness.SignificanceWe are the first to apply an unsupervised approach to offer insights from a biomedical engineering perspective on these three types of organ failure, and publish the pre-trained embeddings for future transfer learning.