GRAM: Graph-based Attention Model for Healthcare Representation Learning

TL;DR

GRAM is a graph-based attention model that enhances healthcare prediction accuracy and interpretability by integrating medical ontologies with electronic health records, especially effective with limited data.

Contribution

Introduces GRAM, a novel attention-based model that incorporates hierarchical medical knowledge to improve healthcare prediction and interpretability.

Findings

GRAM outperforms RNN in disease prediction accuracy.

GRAM requires less training data for effective learning.

Medical concept representations by GRAM align with medical ontologies.

Abstract

Deep learning methods exhibit promising performance for predictive modeling in healthcare, but two important challenges remain: -Data insufficiency:Often in healthcare predictive modeling, the sample size is insufficient for deep learning methods to achieve satisfactory results. -Interpretation:The representations learned by deep learning methods should align with medical knowledge. To address these challenges, we propose a GRaph-based Attention Model, GRAM that supplements electronic health records (EHR) with hierarchical information inherent to medical ontologies. Based on the data volume and the ontology structure, GRAM represents a medical concept as a combination of its ancestors in the ontology via an attention mechanism. We compared predictive performance (i.e. accuracy, data needs, interpretability) of GRAM to various methods including the recurrent neural network (RNN) in two sequential diagnoses prediction tasks and one heart failure prediction task. Compared to the basic RNN, GRAM achieved 10% higher accuracy for predicting diseases rarely observed in the training data and 3% improved area under the ROC curve for predicting heart failure using an order of magnitude less training data. Additionally, unlike other methods, the medical concept representations learned by GRAM are well aligned with the medical ontology. Finally, GRAM exhibits intuitive attention behaviors by adaptively generalizing to higher level concepts when facing data insufficiency at the lower level concepts.