Technical report: Linking the scientific and clinical data with KI2NA-LHC

TL;DR

This paper presents KI2NA-LHC, a system integrating clinical and scientific data to support clinicians and researchers with contextualized information and hypothesis testing in life sciences.

Contribution

It introduces the architecture and implementation of KI2NA-LHC, a framework linking clinical records with scientific data for enhanced medical research and practice.

Findings

Proposed architecture of KI2NA-LHC system

Use scenarios demonstrating clinical and research applications

Methodology for evaluating the framework

Abstract

We introduce a use case and propose a system for data and knowledge integration in life sciences. In particular, we focus on linking clinical resources (electronic patient records) with scientific documents and data (research articles, biomedical ontologies and databases). Our motivation is two-fold. Firstly, we aim to instantly provide scientific context of particular patient cases for clinicians in order for them to propose treatments in a more informed way. Secondly, we want to build a technical infrastructure for researchers that will allow them to semi-automatically formulate and evaluate their hypothesis against longitudinal patient data. This paper describes the proposed system and its typical usage in a broader context of KI2NA, an ongoing collaboration between the DERI research institute and Fujitsu Laboratories. We introduce an architecture of the proposed framework called KI2NA-LHC (for Linked Health Care) and outline the details of its implementation. We also describe typical usage scenarios and propose a methodology for evaluation of the whole framework. The main goal of this paper is to introduce our ongoing work to a broader expert audience. By doing so, we aim to establish an early-adopter community for our work and elicit feedback we could reflect in the development of the prototype so that it is better tailored to the requirements of target users.