An Integrated Knowledge Graph for Life Quality and Survival Rate and Its Application in Decision Support

A consistent integration of quality of life and survival rate is critical to optimal patient-centered clinical decision support. Whilst research work in these independent domains has achieved considerable results, an integrated solution is missing in literature. In this paper, we propose an ontology to standardize the structure of quality-of-life assessment knowledge. It serves as a foundation on the basis of which we are able to map and associated clinical guideline, and online patient social site datasets weighted the decision support knowledge graph which represents differences. The survival benefits against various treatments can then be calculated, and quality of life mapped simultaneously, in line with patient's clinical conditions and personal preferences. A prototype system is designed and implemented to demonstrate the feasibility of the approach for adjuvant postoperative breast cancer treatment decision support.