Fine-grained Patient Similarity Measuring using Deep Metric Learning

Abstract

Patient similarity measuring plays a significant role in many healthcare applications, such as cohort study and treatment comparative effectiveness research. Existing methods mainly rely on supervised metric learning method to study patient similarity from Electronic Health Records (EHRs), facing the challenge of differentiating patients with a large number of fine-grained disease categories. Deep metric learning has gained noticeable success in fine-grained image categorization problem, however, it cannot be directly applied to classification of patients with hierarchical disease labels. In this paper, we present a novel three layer patient similarity deep metric learning framework (PSDML) by optimizing quadruple loss improved from triplet loss, to learn an embedding distance for disease classification among the patients. The context semantic relation of multi diagnosis labels encoding by ICD-10 is taken into account to compute the supervised distance of patients. To solve the diagnosis class imbalance, patient tuples that violate deep metric learning framework loss constraints are chosen prior as samples to accelerate the convergence of the neural network. We conducted KNN multi label classification experiment using the learned similarity metric on the real EHRs about stroke disease collected by Chinese Stroke Data Center. The results demonstrate substantial improvement over the baselines.

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