# PDD Graph: Bridging Electronic Medical Records and Biomedical Knowledge Graphs via Entity Linking

arXiv: https://arxiv.org/abs/1707.05340 · HTML: https://mlsanity.com/paper/1707.05340/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2017.

## Built on by (most cited first, 2 of 2)

- [Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph](https://mlsanity.com/paper/2204.13329/) (2022)
- [Explainable Sparse Knowledge Graph Completion via High-order Graph Reasoning Network](https://mlsanity.com/paper/2207.07503/) (2022)

## Abstract (arXiv)

Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge. Facing with patient's symptoms, experienced caregivers make right medical decisions based on their professional knowledge that accurately grasps relationships between symptoms, diagnosis and corresponding treatments. In this paper, we aim to capture these relationships by constructing a large and high-quality heterogenous graph linking patients, diseases, and drugs (PDD) in EMRs. Specifically, we propose a novel framework to extract important medical entities from MIMIC-III (Medical Information Mart for Intensive Care III) and automatically link them with the existing biomedical knowledge graphs, including ICD-9 ontology and DrugBank. The PDD graph presented in this paper is accessible on the Web via the SPARQL endpoint, and provides a pathway for medical discovery and applications, such as effective treatment recommendations.