Individualized Knowledge Graph: A Viable Informatics Path to Precision Medicine

Abstract

We present here a vision of individualized Knowledge Graphs (iKGs) in cardiovascular medicine: a modern informatics platform of exchange and inquiry that comprehensively integrates biological knowledge with medical histories and health outcomes of individual patients. We envision that this could transform how clinicians and scientists together discover, communicate, and apply new knowledge.

Keywords: artificial intelligence; data mining; informatics; machine learning; precision medicine.

Figure.. Overview of the 2-pronged integrated architecture of individualized Knowledge Graphs (iKGs).

A 2-pronged system with Clinical Practice and Translational Research Arms is shown. The Clinical Practice Arm could support clinicians in accessing and interactively viewing observations and phenotypic abstractions of a patient (eg, electronic health record [EHR], biomedical literature, and mHealth data). The Translational Research Arm could benefit researchers by providing tools for analyzing biological observations and pathways (eg, molecular, omics, laboratory, and imaging data). Three main interacting elements of iKGs, representing opportunities outlined in the text, are defined here. First, shown in blue (left) are the different data sources (eg, Data source 1–3) and types (eg, omics, imaging, and text data). Second, shown in yellow (middle) is an exemplary integrated platform addressing both portability and interoperability, and detailing how data sources could be used to construct iKGs. Last, shown in red (right) is how one may develop use cases and applications to validate iKGs, thereby supporting clinical translation.