Toward a Unified Graph-Based Representation of Medical Data for Precision Oncology Medicine

TL;DR

This paper introduces a unified graph-based model that integrates genetic data and medical records into a knowledge graph, enabling enhanced insights and explanations in precision oncology medicine.

Contribution

It proposes a novel unified graph representation combining diverse medical data sources into a knowledge graph for improved oncology insights.

Findings

Enhanced understanding of oncology through integrated data

Reduction of medical tasks to known computational problems

Efficient algorithms applied to medical data analysis

Abstract

We present a new unified graph-based representation of medical data, combining genetic information and medical records of patients with medical knowledge via a unique knowledge graph. This approach allows us to infer meaningful information and explanations that would be unavailable by looking at each data set separately. The systematic use of different databases, managed throughout the built knowledge graph, gives new insights toward a better understanding of oncology medicine. Indeed, we reduce some useful medical tasks to well-known problems in theoretical computer science for which efficient algorithms exist.