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Data Analysis for Patients with Sleep Apnea Syndrome: A Complex Network Approach

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Soft Computing Applications (SOFA 2014)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 356))

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Abstract

Network science is an emerging paradigm branching over more and more aspects of physical, biological, and social phenomena. One such branch, which has brought cutting edge contributions to medical science, is the field of network medicine. Along this direction, our proposed study sets out to identify specific patterns of developing obstructive sleep apnea (OSA), by taking into consideration the multiple connections between risk factors in a relevant population of patients. For this purpose, we create a social network of patients based on their common medical conditions and obtain a community based society which pinpoints to specific—and previously uncharted—patterns of developing OSA. Eventually, this insight should create incentives for predicting the apnea stage for any new patient by evaluating its network topological position.

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Author information

Authors and Affiliations

  1. Department of Computers and Information Technology, Politehnica University of Timisoara, Bd. Vasile Parvan 2, 300223, Timisoara, Romania

    Alexandru Topirceanu, Mihai Udrescu & Razvan Avram

  2. Department of Pneumology, Victor Babes University of Medicine and Pharmacy, Piata E. Murgu 2, 300041, Timisoara, Romania

    Stefan Mihaicuta

Authors
  1. Alexandru Topirceanu
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  2. Mihai Udrescu
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  3. Razvan Avram
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  4. Stefan Mihaicuta
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Corresponding author

Correspondence to Alexandru Topirceanu .

Editor information

Editors and Affiliations

  1. Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  2. Faculty of Science and Technology, Data Science Institute, Bournemouth University, Poole, United Kingdom

    Lakhmi C. Jain

  3. University of Belgrade, Belgrade, Serbia

    Branko Kovačević

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Topirceanu, A., Udrescu, M., Avram, R., Mihaicuta, S. (2016). Data Analysis for Patients with Sleep Apnea Syndrome: A Complex Network Approach. In: Balas, V., C. Jain, L., Kovačević, B. (eds) Soft Computing Applications. SOFA 2014. Advances in Intelligent Systems and Computing, vol 356. Springer, Cham. https://doi.org/10.1007/978-3-319-18296-4_19

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  • DOI: https://doi.org/10.1007/978-3-319-18296-4_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18295-7

  • Online ISBN: 978-3-319-18296-4

  • eBook Packages: EngineeringEngineering (R0)

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