Digital Twin Using Clinical Personal Knowledge Graphs: Toward Precision Medicine

Abstract

The medicine used today is often referred to as “one-size-fits-all” medicine, designed to treat the average person. However, such approach could lead to ineffective or suboptimal results, or even serious side effects for patients who deviate significantly from the average. To alleviate such problem, the concept of precision medicine has emerged, aiming to provide treatment tailored to the individual patient's characteristics. In order for precision medicine to be effective, some requirements such as comprehensive data collection, data integration, and data analysis should be satisfied. In this paper, we propose health digital twin (DT) based on clinical personal knowledge graph (PKG). Since digital twin is continuously synchronized with patient's body condition through collected data, it is possible to describe his/her body function accurately. The proposed PKG is based on the semantics of SNOMED CT and LOINC, which satisfies the requirements. In our approach, we added chronological collection of PKGs to show the changes of the health conditions of the patients to improve accuracy.

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