Fundamentals of Semantic Web Technologies in Medical Environments: a case in breast cancer risk estimation

TL;DR

This paper discusses the application of Semantic Web technologies, including linked data and rule engines, to improve breast cancer risk estimation and data integration in medical environments for early diagnosis and patient safety.

Contribution

It introduces a framework utilizing Semantic Web tools for integrating medical data and automating risk estimation processes in breast cancer diagnosis.

Findings

Enhanced data integration across departments

Automated risk estimation process

Improved patient safety through orchestration

Abstract

Risk estimation of developing breast cancer poses as the first prevention method for early diagnosis. Furthermore, data integration from different departments involved in the process plays a key role. In order to guarantee patient safety, the whole process should be orchestrated and monitored automatically. Support for the solution will be a linked data cloud, composed by all the departments that take part in the process, combined with rule engines.