JMIR Publications

ABSTRACT

Background:

Patient Representation Learning aims to learn features, also called representations, from input sources automatically, often in an unsupervised manner, for use in predictive models. This obviates the need for cumbersome, time- and resource-intensive manual feature engineering, especially from unstructured data such as text, images or graphs. Most previous techniques have used neural network based autoencoders to learn patient representations, primarily from clinical notes in Electronic Medical Records (EMR). Knowledge Graphs (KG), with clinical entities as nodes and their relations as edges, can be extracted automatically from biomedical literature, and provide complementary information to EMR data that have been found to provide valuable predictive signals.

Objective:

We evaluate the efficacy of Collective Matrix Factorization (CMF) - both classical variants and a recent neural architecture called Deep CMF (DCMF) - in integrating heterogeneous data sources from EMR and KG to obtain patient representations for Clinical Decision Support Tasks.

Methods:

Using a recent formulation of obtaining graph representations through matrix factorization, within the context of CMF, we infuse auxiliary information during patient representation learning. We also extend the DCMF architecture to create a task-specific end-to-end model that learns to simultaneously find effective patient representations and predict. We compare the efficacy of such a model to that of first learning unsupervised representations and then independently learning a predictive model. We evaluate patient representation learning using CMF-based methods and autoencoders for two clinical decision support tasks on a large EMR dataset.

Results:

Our experiments show that DCMF provides a seamless way to integrate multiple sources of data to obtain patient representations, both in unsupervised and supervised settings. Its performance in single-source settings is comparable to that of previous autoencoder-based representation learning methods. When DCMF is used to obtain representations from a combination of EMR and KG, where most previous autoencoder-based methods cannot be used directly, its performance is superior to that of previous non-neural methods for CMF. Infusing information from KGs into patient representations using DCMF was found to improve downstream predictive performance.

Conclusions:

Our experiments indicate that DCMF is a versatile model that can be used to obtain representations from single and multiple data sources, and to combine information from EMR data and Knowledge Graphs. Further, DCMF can be used to learn representations in both supervised and unsupervised settings. Thus, DCMF offers an effective way of integrating heterogeneous data sources and infusing auxiliary knowledge into patient representations.