Monte Carlo Optimization of Liver Machine Perfusion Temperature Policies

Angelo Lucia, Korkut Uygun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationMachine Learning, Optimization, and Data Science - 8th International Workshop, LOD 2022, Revised Selected Papers
EditorsGiuseppe Nicosia, Giovanni Giuffrida, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Panos Pardalos, Giuseppe Di Fatta, Renato Umeton
PublisherSpringer Science and Business Media Deutschland GmbH
Pages296-303
Number of pages8
ISBN (Print)9783031258909
DOIs
StatePublished - 2023
Event8th International Conference on Machine Learning, Optimization, and Data Science, LOD 2022, held in conjunction with the 2nd Advanced Course and Symposium on Artificial Intelligence and Neuroscience, ACAIN 2022 - Certosa di Pontignano, Italy
Duration: Sep 18 2022Sep 22 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13811 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Machine Learning, Optimization, and Data Science, LOD 2022, held in conjunction with the 2nd Advanced Course and Symposium on Artificial Intelligence and Neuroscience, ACAIN 2022
Country/TerritoryItaly
CityCertosa di Pontignano
Period9/18/229/22/22

Bibliographical note

Funding Information:
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.

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

Keywords

  • Machine perfusion
  • Monte Carlo
  • Multi-objective optimization

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