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Simulation-Based Episodes of Care Data Synthetization for Chronic Disease Patients

  • Conference paper
  • First Online:
Knowledge Representation for Health Care (ProHealth 2016, KR4HC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10096))

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.

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Notes

  1. 1.

    The value later-year determines the EoC time-out termination.

  2. 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.

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Authors and Affiliations

  1. Research Group on Artificial Intelligence, Universitat Rovira i Virgili, Tarragona, Spain

    David Riaño & Alberto Fernández-Pérez

Authors
  1. David Riaño
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  2. Alberto Fernández-Pérez
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Correspondence to David Riaño .

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Editors and Affiliations

  1. Rovira i Virgili University, Tarragona, Spain

    David Riaño

  2. University of Erlangen and Nuremberg, Erlangen, Germany

    Richard Lenz

  3. University of Ulm, Ulm, Germany

    Manfred Reichert

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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

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  • DOI: https://doi.org/10.1007/978-3-319-55014-5_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55013-8

  • Online ISBN: 978-3-319-55014-5

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