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K-Medoids-Surv: A Patients Risk Stratification Algorithm Considering Censored Data

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Book cover Artificial Intelligence and Soft Computing (ICAISC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13589))

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Abstract

Traditional survival analysis estimates the instantaneous failure rate of an event and predicts survival probabilities distributions. In fact, in a set of censored data there may exist several sub-populations with various risk profiles or survival distributions, for which regular survival analysis approaches do not take into consideration. Consequently, there is a need for discovering such sub-populations with unambiguous risk profiles and survival distributions. In this work, we propose a modified version of the K-Medoids algorithm which can be used to efficiently cluster censored data and identify diverse groups with distinct lifetime distributions.

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Acknowledgements

The research leading to the results presented in this paper has received funding from the European Union’s funded Project iHelp under grant agreement no 101017441.

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Authors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    George Marinos, Chrysostomos Symvoulidis & Dimosthenis Kyriazis

Authors
  1. George Marinos
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  2. Chrysostomos Symvoulidis
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  3. Dimosthenis Kyriazis
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Corresponding author

Correspondence to George Marinos .

Editor information

Editors and Affiliations

  1. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Marinos, G., Symvoulidis, C., Kyriazis, D. (2023). K-Medoids-Surv: A Patients Risk Stratification Algorithm Considering Censored Data. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2022. Lecture Notes in Computer Science(), vol 13589. Springer, Cham. https://doi.org/10.1007/978-3-031-23480-4_11

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  • DOI: https://doi.org/10.1007/978-3-031-23480-4_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23479-8

  • Online ISBN: 978-3-031-23480-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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