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Machine Learning Applications in Solid Organ Transplantation and Related Complications
The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning promises to inform clinical decision making by de...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8481939/ https://www.ncbi.nlm.nih.gov/pubmed/34603324 http://dx.doi.org/10.3389/fimmu.2021.739728 |
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author | Balch, Jeremy A. Delitto, Daniel Tighe, Patrick J. Zarrinpar, Ali Efron, Philip A. Rashidi, Parisa Upchurch, Gilbert R. Bihorac, Azra Loftus, Tyler J. |
author_facet | Balch, Jeremy A. Delitto, Daniel Tighe, Patrick J. Zarrinpar, Ali Efron, Philip A. Rashidi, Parisa Upchurch, Gilbert R. Bihorac, Azra Loftus, Tyler J. |
author_sort | Balch, Jeremy A. |
collection | PubMed |
description | The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning promises to inform clinical decision making by deciphering prodigious amounts of available data. This paper reviews current research describing how algorithms have the potential to augment clinical practice in solid organ transplantation. We provide a general introduction to different machine learning techniques, describing their strengths, limitations, and barriers to clinical implementation. We summarize emerging evidence that recent advances that allow machine learning algorithms to predict acute post-surgical and long-term outcomes, classify biopsy and radiographic data, augment pharmacologic decision making, and accurately represent the complexity of host immune response. Yet, many of these applications exist in pre-clinical form only, supported primarily by evidence of single-center, retrospective studies. Prospective investigation of these technologies has the potential to unlock the potential of machine learning to augment solid organ transplantation clinical care and health care delivery systems. |
format | Online Article Text |
id | pubmed-8481939 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84819392021-10-01 Machine Learning Applications in Solid Organ Transplantation and Related Complications Balch, Jeremy A. Delitto, Daniel Tighe, Patrick J. Zarrinpar, Ali Efron, Philip A. Rashidi, Parisa Upchurch, Gilbert R. Bihorac, Azra Loftus, Tyler J. Front Immunol Immunology The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning promises to inform clinical decision making by deciphering prodigious amounts of available data. This paper reviews current research describing how algorithms have the potential to augment clinical practice in solid organ transplantation. We provide a general introduction to different machine learning techniques, describing their strengths, limitations, and barriers to clinical implementation. We summarize emerging evidence that recent advances that allow machine learning algorithms to predict acute post-surgical and long-term outcomes, classify biopsy and radiographic data, augment pharmacologic decision making, and accurately represent the complexity of host immune response. Yet, many of these applications exist in pre-clinical form only, supported primarily by evidence of single-center, retrospective studies. Prospective investigation of these technologies has the potential to unlock the potential of machine learning to augment solid organ transplantation clinical care and health care delivery systems. Frontiers Media S.A. 2021-09-16 /pmc/articles/PMC8481939/ /pubmed/34603324 http://dx.doi.org/10.3389/fimmu.2021.739728 Text en Copyright © 2021 Balch, Delitto, Tighe, Zarrinpar, Efron, Rashidi, Upchurch, Bihorac and Loftus https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Balch, Jeremy A. Delitto, Daniel Tighe, Patrick J. Zarrinpar, Ali Efron, Philip A. Rashidi, Parisa Upchurch, Gilbert R. Bihorac, Azra Loftus, Tyler J. Machine Learning Applications in Solid Organ Transplantation and Related Complications |
title | Machine Learning Applications in Solid Organ Transplantation and Related Complications |
title_full | Machine Learning Applications in Solid Organ Transplantation and Related Complications |
title_fullStr | Machine Learning Applications in Solid Organ Transplantation and Related Complications |
title_full_unstemmed | Machine Learning Applications in Solid Organ Transplantation and Related Complications |
title_short | Machine Learning Applications in Solid Organ Transplantation and Related Complications |
title_sort | machine learning applications in solid organ transplantation and related complications |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8481939/ https://www.ncbi.nlm.nih.gov/pubmed/34603324 http://dx.doi.org/10.3389/fimmu.2021.739728 |
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