Knowledge-Based Patient Data Generation
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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.
KeywordsPatient Data Domain Knowledge Electronic Health Records Female Breast Cancer Biomedical Publication
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- 1.Beale, T.: Archetypes: Constraint-based domain models for future-proof information systems. In: OOPSLA 2002 Workshop on Behavioural Semantics (2002)Google Scholar
- 2.Bucur, A., ten Teije, A., van Harmelen, F., Tagni, G., Kondylakis, H., van Leeuwen, J., Schepper, K.D., Huang, Z.: Formalization of eligibility conditions of CT and a patient recruitment method, D6.1. Technical report, EURECA Project (2012)Google Scholar
- 3.Buczak, A., Babin, S., Moniz, L.: Data-driven approach for creating synthetic electronic medical records. BMC Medical Informatics and Decision Making 10(59) (2010)Google Scholar
- 5.Fensel, D., van Harmelen, F., Andersson, B., Brennan, P., Cunningham, H., Della Valle, E., Fischer, F., Huang, Z., Kiryakov, A., Lee, T., School, L., Tresp, V., Wesner, S., Witbrock, M., Zhong, N.: Towards LarKC: a platform for web-scale reasoning. In: Proceedings of the IEEE International Conference on Semantic Computing (ICSC 2008). IEEE Computer Society Press, CA (2008)Google Scholar
- 7.Huang, Z., ten Teije, A., van Harmelen, F.: SemanticCT: A semantically enabled clinical trial system. In: Lenz, R., Mikszh, S., Peleg, M., Reichert, M., ten Teije, D.R.A. (eds.) Process Support and Knowledge Representation in Health Care. LNCS (LNAI), Springer (2013)Google Scholar
- 8.Moniz, L., Buczak, A.L., Hung, L., Babin, S., Dorko, M., Lombardo, J.: Construction and validation of synthetic electronic medical records. Journal of Public Health 1(1), 1–36 (2009)Google Scholar
- 9.Spackman, K.: Managing clinical terminology hierarchies using algorithmic calculation of subsumption: Experience with snomed-rt. Journal of the American Medical Informatics Association (2000)Google Scholar
- 10.Witbrock, M., Fortuna, B., Bradesko, L., Kerrigan, M., Bishop, B., van Harmelen, F., ten Teije, A., Oren, E., Momtchev, V., Tenschert, A., Cheptsov, A., Roller, S., Gallizo, G.: D5.3.1 - requirements analysis and report on lessons learned during prototyping. Larkc project deliverable (June 2009)Google Scholar
- 11.Zhang, M., Huang, Z., Gu, J.: Visual interface tools for advanced patient data generator. In: Chinese Digital Medicine (to appear, 2013)Google Scholar