Abstract
Primary care studies related to chronic diseases and their treatment use to be based on the analysis of large amounts of episodes of care (EoC) in order to check standard’s compliance, improve protocols, or perform quality and cost studies. In these EoC, data related to the patient condition and treatment are registered along the different encounters between the patient and the health care professionals. However, EoC data analysis is subject to some limitations such as data availability, reliability, and appropriateness, aside of multiple legal issues. Several studies exist to surpass these limitations with software technologies that synthesize realistic clinical data. Two are the main approaches: data-driven, or the construction of quantitative models from data about retrospective clinical cases, and knowledge-driven, or the construction of qualitative (semantic) models from the accumulation of medical evidences.
These approaches have some limitations that we aimed to surpass with a computer-based virtual-patient simulation system to synthesize EoC data for chronic diseases that have been applied to generate data about long-term treatment of hypertension cases. Unlike other previous systems, our approach takes advantage of the pros of both, the data- and the knowledge-driven approaches. In this paper we introduce the system, apply it to produce EoC synthetic data about virtual patients with arterial hypertension, and identify a limited number of modifiers of the system that allow adaptation and, therefore, the progressive improvement of the synthesized data generated.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The value later-year determines the EoC time-out termination.
- 2.
Chronic diseases last for long periods, so treatment duration depends on the study time interval mainly and it should not be interpreted as the average curing time.
References
Buczak, A.L., Moniz, L.J., Copeland, J., et al.: Data-driven hybrid method for synthetic electronic medical records generation. In: Proceedings of the IDAMAP 2008, pp. 81–86 (2008)
Buczak, A.L., Moniz, L.J., Feighner, B.H., Lombardo, J.S.: Mining electronic medical records for patient care patterns. In: Proceedings of the IEEE Symposium CIDM 2009, pp. 146–153 (2009)
Moniz, L., Buczak, A.L., Hung, L., et al.: Constuction and validation of synthetic electronic medical records. Online J. Public Health Inform. 1(1), e2 (2009)
Buczac, A.L., Babin, S., Moniz, L.: Data-driven approach for creating synthetic electronic medical records. Med. Inform. Decis. Making 10, 59 (2010)
Dube, K., Gallagher, T.: Approach and method for generating realistic synthetic electronic healthcare records for secondary use. In: Gibbons, J., MacCaull, W. (eds.) FHIES 2013. LNCS, vol. 8315, pp. 69–86. Springer, Heidelberg (2014). doi:10.1007/978-3-642-53956-5_6
Huang, Z., Harmelen, F., Teije, A., Dentler, K.: Knowledge-based patient data generation. In: Riaño, D., Lenz, R., Miksch, S., Peleg, M., Reichert, M., Teije, A. (eds.) KR4HC/ProHealth 2013. LNCS (LNAI), vol. 8268, pp. 83–96. Springer, Heidelberg (2013). doi:10.1007/978-3-319-03916-9_7
Real, F., Riaño, D., Alonso, J.R.: A patient simulation model based on decision tables for emergency shocks. In: Riaño, D., Lenz, R., Miksch, S., Peleg, M., Reichert, M., Teije, A. (eds.) KR4HC 2015. LNCS (LNAI), vol. 9485, pp. 21–33. Springer, Heidelberg (2015). doi:10.1007/978-3-319-26585-8_2
Riaño, D.: A systematic analysis of medical decisions: how to store knowledge and experience in decision tables. In: Riaño, D., Teije, A., Miksch, S. (eds.) KR4HC 2011. LNCS (LNAI), vol. 6924, pp. 23–36. Springer, Heidelberg (2012). doi:10.1007/978-3-642-27697-2_2
Talbot, T.B., Sagae, K., John, B., Rizzo, A.A.: Sorting out the virtual patient. Int. J. Gaming Comput. Mediated Simul. 4(3), 1–19 (2012)
Real, F., Riaño, D., Alonso, J.R.: Training residents in the application of clinical guidelines for differential diagnosis of the most frequent causes of arterial hypertension with decision tables. In: Miksch, S., Riaño, D., Teije, A. (eds.) KR4HC 2014. LNCS (LNAI), vol. 8903, pp. 147–159. Springer, Heidelberg (2014). doi:10.1007/978-3-319-13281-5_11
Real, F.: Use of decision tables to model assistance knowledge to train medical residents. Universitat Rovira i Virgili. Ph.D. dissertation (2016)
Riaño, D., Collado, A.: Model-based combination of treatments for the management of chronic comorbid patients. In: Peek, N., Marín Morales, R., Peleg, M. (eds.) AIME 2013. LNCS (LNAI), vol. 7885, pp. 11–16. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38326-7_2
Chowdhury HMS. CDML: A Chronic Disease Management. MSc dissertation (2013)
Shiffman, R.N.: Representation of clinical practice guidelines in conventional and augmented decision tables. J. Am. Med. Inform. Assoc. 4(5), 382–393 (1997)
Shiffman, R.N., Greenes, R.A.: Use of augmented decision tables to convert probabilistic data into clinical algorithms for the diagnosis of appendicitis. In: Proceedings of the Annual Symposium on Computer Application in Medical Care, pp. 686–690 (1991)
Bielza, C., Pozo, Juan, A.,Fernández, Lucas, P.: Finding and explaining optimal treatments. In: Dojat, M., Keravnou, Elpida, T., Barahona, P. (eds.) AIME 2003. LNCS (LNAI), vol. 2780, pp. 299–303. Springer, Heidelberg (2003). doi:10.1007/978-3-540-39907-0_41
Chobanian, A.V., et al.: The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure (2003)
Bohada, J.A.: Automatic production and integration of knowledge to the support of the decision and planning activities in medical-clinical diagnosis, treatment and prognosis. Ph.D. dissertation (2012)
López-Vallverdú, J.A.: Knowledge-based incremental induction of clinical algorithms. Ph.D. dissertation (2012)
Riaño, D., Real, F., et al.: An ontology-based personalization of health-care knowledge to support clinical decisions for chronically ill patients. JBI 45(3), 429–446 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Riaño, D., Fernández-Pérez, A. (2017). Simulation-Based Episodes of Care Data Synthetization for Chronic Disease Patients. In: Riaño, D., Lenz, R., Reichert, M. (eds) Knowledge Representation for Health Care. ProHealth KR4HC 2016 2016. Lecture Notes in Computer Science(), vol 10096. Springer, Cham. https://doi.org/10.1007/978-3-319-55014-5_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-55014-5_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55013-8
Online ISBN: 978-3-319-55014-5
eBook Packages: Computer ScienceComputer Science (R0)