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Monte Carlo Optimization of Liver Machine Perfusion Temperature Policies

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  • First Online:
Machine Learning, Optimization, and Data Science (LOD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13811))

Abstract

In this work, a constrained multi-objective function formulation of liver machine perfusion (MP) based on widely accepted viability criteria and network metabolic efficiency is described. A novel Monte Carlo method is used to improve machine perfusion (MP) performance by finding optimal temperature policies for hypothermic machine perfusion (HMP), mid-thermic machine perfusion (MMP), and subnormothermic machine perfusion (SNMP). It is shown that the multi-objective function formulation can exhibit multiple maxima, that greedy optimization can get stuck at a local optimum, and that Monte Carlo optimization finds the best temperature policy in each case.

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Acknowledgement

This material is partially based upon work supported by the National Science Foundation under Grant No. EEC 1941543. Support from the US National Institutes of Health (grants R01DK096075 and R01DK114506) and the Shriners Hospitals for Children is gratefully acknowledged.

Author information

Authors and Affiliations

  1. Department of Chemical Engineering, University of Rhode Island, Kingston, RI, 02881, USA

    Angelo Lucia

  2. Center for Engineering in Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA

    Korkut Uygun

Authors
  1. Angelo Lucia
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  2. Korkut Uygun
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Corresponding author

Correspondence to Angelo Lucia .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  6. Free University of Bozen-Bolzano, Bolzano, Italy

    Giuseppe Di Fatta

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Lucia, A., Uygun, K. (2023). Monte Carlo Optimization of Liver Machine Perfusion Temperature Policies. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13811. Springer, Cham. https://doi.org/10.1007/978-3-031-25891-6_22

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  • DOI: https://doi.org/10.1007/978-3-031-25891-6_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25890-9

  • Online ISBN: 978-3-031-25891-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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