Toward a Unified Graph-Based Representation of Medical Data for Precision Oncology Medicine
This work addresses the challenge of data fragmentation in oncology medicine for clinicians and researchers, though it appears incremental as it builds on existing graph-based methods.
The paper tackles the problem of integrating diverse medical data for precision oncology by proposing a unified graph-based representation that combines genetic information, medical records, and medical knowledge into a knowledge graph, resulting in new insights and explanations for oncology medicine.
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.