From Data to Wisdom: Biomedical Knowledge Graphs for Real-World Data Insights

Abstract

Graph data models are an emerging approach to structure clinical and biomedical information. These models offer intriguing opportunities for novel approaches in healthcare, such as disease phenotyping, risk prediction, and personalized precision care. The combination of data and information in a graph model to create knowledge graphs has rapidly expanded in biomedical research, but the integration of real-world data from the electronic health record has been limited. To broadly apply knowledge graphs to EHR and other real-world data, a deeper understanding of how to represent these data in a standardized graph model is needed. We provide an overview of the state-of-the-art research for clinical and biomedical data integration and summarize the potential to accelerate healthcare and precision medicine research through insight generation from integrated knowledge graphs.

Dark Data in Real-World Evidence: Challenges, Implications, and the Imperative of Data Literacy in Medical Research

“…4 ). 31 32 33 In other words, data analysis is advantageous for professional data scientists, but it is often enough to apply simple statistical tools rather than complex expertise to data analysis. Therefore, the technical process of extracting “information” from “data” is not too difficult, given the technological statistical advancements used to organize, structure, categorize, and calculate data.…”

“…Therefore, the technical process of extracting “information” from “data” is not too difficult, given the technological statistical advancements used to organize, structure, categorize, and calculate data. 33 However, the pivotal facet is that to solve the specific clinical problem targeted through data analysis, interpreting the results based on medical concepts and experiences is essential and critical before applying them to actual clinical practice. This progression of clinical data literacy transforms information into clinically deployable “knowledge” through expert concepts, ideas, learning, and discussion.…”

“…This progression of clinical data literacy transforms information into clinically deployable “knowledge” through expert concepts, ideas, learning, and discussion. 33 Presently, numerous big data or AI studies yield promising results; however, there are several cases where clinical commercialization is limited because they stopped at information extraction without clinical interpretation. Such studies often neglect to progress to the knowledge stage owing to inadequate reflection of medical expert experiences and treatment concepts.…”