Knowledge-Based Patient Data Generation

The development and investigation of medical applications require patient data from various Electronic Health Records (EHRs) or Clinical Records (CRs). However, in practice, patient data is and should be protected to avoid unauthorized access or publicity, because of many reasons including privacy, security, ethics, and confidentiality. Thus, many researchers and developers encounter the problem to access required patient data for their research or to make patient data available for example to demonstrate the reproducibility of their results. In this paper, we propose a knowledge-based approach of synthesizing large scale patient data. Our main goal is to make the generated patient data as realistic as possible, by using domain knowledge to control the data generation process. Such domain knowledge can be collected from biomedical publications such as those included in PubMed, from medical textbooks, or web resources (e.g. Wikipedia and medical websites). Collected knowledge is formalized in the Patient Data Definition Language (PDDL) for the patient data generation. We have implemented the proposed approach in our Advanced Patient Data Generator (APDG). We have used APDG to generate large scale data for breast cancer patients in the experiments of SemanticCT, a semantically-enabled system for clinical trials. The results show that the generated patient data is useful for various tests in the system.