MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare

TL;DR

MiME introduces a multilevel embedding approach for EHR data that leverages inherent data structure and auxiliary tasks, significantly improving predictive healthcare performance especially on smaller datasets.

Contribution

This paper presents MiME, a novel multilevel embedding method for EHR data that does not require external labels and effectively models the data's inherent structure.

Findings

MiME outperforms baseline methods in heart failure and disease prediction tasks.

MiME achieves a 15% relative gain in PR-AUC on small datasets.

MiME is particularly effective when training data volume is limited.

Abstract

Deep learning models exhibit state-of-the-art performance for many predictive healthcare tasks using electronic health records (EHR) data, but these models typically require training data volume that exceeds the capacity of most healthcare systems. External resources such as medical ontologies are used to bridge the data volume constraint, but this approach is often not directly applicable or useful because of inconsistencies with terminology. To solve the data insufficiency challenge, we leverage the inherent multilevel structure of EHR data and, in particular, the encoded relationships among medical codes. We propose Multilevel Medical Embedding (MiME) which learns the multilevel embedding of EHR data while jointly performing auxiliary prediction tasks that rely on this inherent EHR structure without the need for external labels. We conducted two prediction tasks, heart failure prediction and sequential disease prediction, where MiME outperformed baseline methods in diverse evaluation settings. In particular, MiME consistently outperformed all baselines when predicting heart failure on datasets of different volumes, especially demonstrating the greatest performance improvement (15% relative gain in PR-AUC over the best baseline) on the smallest dataset, demonstrating its ability to effectively model the multilevel structure of EHR data.