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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 language | English (US) |
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Title of host publication | Machine Learning, Optimization, and Data Science - 8th International Workshop, LOD 2022, Revised Selected Papers |
Editors | Giuseppe Nicosia, Giovanni Giuffrida, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Panos Pardalos, Giuseppe Di Fatta, Renato Umeton |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 296-303 |
Number of pages | 8 |
ISBN (Print) | 9783031258909 |
DOIs | |
State | Published - 2023 |
Event | 8th 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 2022 → Sep 22 2022 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 13811 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 8th 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 |
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Country/Territory | Italy |
City | Certosa di Pontignano |
Period | 9/18/22 → 9/22/22 |
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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ATP-Bio: NSF Engineering Research Center for Advanced Technologies for the Preservation of Biological Systems (ATP-Bio)
Bischof, J. C., Toner, M., Roehrig, G. H., Aguilar, G. & Healy, K. E.
National Science Foundation, NSF
9/1/20 → 8/31/25
Project: Research project