# Representation Learning of EHR Data via Graph-Based Medical Entity Embedding

arXiv: https://arxiv.org/abs/1910.02574 · HTML: https://mlsanity.com/paper/1910.02574/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [Graph Representation Learning in Biomedicine](https://mlsanity.com/paper/2104.04883/) (2021)
- [Zoo Guide to Network Embedding](https://mlsanity.com/paper/2305.03474/) (2023)
- [A Visual Analytics Design for Connecting Healthcare Team Communication to Patient Outcomes](https://mlsanity.com/paper/2401.03700/) (2024)

## Abstract (arXiv)

Automatic representation learning of key entities in electronic health record (EHR) data is a critical step for healthcare informatics that turns heterogeneous medical records into structured and actionable information. Here we propose ME2Vec, an algorithmic framework for learning low-dimensional vectors of the most common entities in EHR: medical services, doctors, and patients. ME2Vec leverages diverse graph embedding techniques to cater for the unique characteristic of each medical entity. Using real-world clinical data, we demonstrate the efficacy of ME2Vec over competitive baselines on disease diagnosis prediction.