Knowledge-Based Patient Data Generation

  • Zhisheng Huang
  • Frank van Harmelen
  • Annette ten Teije
  • Kathrin Dentler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8268)


The development and investigation of medical applications require patient data from various Electronic Health Records (EHR) or Clinical Records (CR). However, in practice, patient data is and should be protected and monitored 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 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 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 are useful for various tests in the system.


Patient Data Domain Knowledge Electronic Health Records Female Breast Cancer Biomedical Publication 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Zhisheng Huang
    • 1
  • Frank van Harmelen
    • 1
  • Annette ten Teije
    • 1
  • Kathrin Dentler
    • 1
  1. 1.Department of Computer ScienceVU UniversityAmsterdamThe Netherlands

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