1
|
Rabindranath M, Naghibzadeh M, Zhao X, Holdsworth S, Brudno M, Sidhu A, Bhat M. Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold? Transplantation 2024; 108:1700-1708. [PMID: 39042768 DOI: 10.1097/tp.0000000000004876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
Medical applications of machine learning (ML) have shown promise in analyzing patient data to support clinical decision-making and provide patient-specific outcomes. In transplantation, several applications of ML exist which include pretransplant: patient prioritization, donor-recipient matching, organ allocation, and posttransplant outcomes. Numerous studies have shown the development and utility of ML models, which have the potential to augment transplant medicine. Despite increasing efforts to develop robust ML models for clinical use, very few of these tools are deployed in the healthcare setting. Here, we summarize the current applications of ML in transplant and discuss a potential clinical deployment framework using examples in organ transplantation. We identified that creating an interdisciplinary team, curating a reliable dataset, addressing the barriers to implementation, and understanding current clinical evaluation models could help in deploying ML models into the transplant clinic setting.
Collapse
Affiliation(s)
- Madhumitha Rabindranath
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
| | - Maryam Naghibzadeh
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Xun Zhao
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Sandra Holdsworth
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Michael Brudno
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Aman Sidhu
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Mamatha Bhat
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
2
|
Syed FJ, Bekbolsynov D, Stepkowski S, Kaur D, Green RC. Maximizing matching, equity and survival in kidney transplantation using molecular HLA immunogenicity quantitation. Comput Biol Med 2024; 174:108452. [PMID: 38640635 DOI: 10.1016/j.compbiomed.2024.108452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 03/11/2024] [Accepted: 04/07/2024] [Indexed: 04/21/2024]
Abstract
HLA matching improves long-term outcomes of kidney transplantation, yet implementation challenges persist, particularly within the African American (Black) patient demographic due to donor scarcity. Consequently, kidney survival rates among Black patients significantly lag behind those of other racial groups. A refined matching scheme holds promise for improving kidney survival, with prioritized matching for Black patients potentially bolstering rates of HLA-matched transplants. To facilitate quantity, quality and equity in kidney transplants, we propose two matching algorithms based on quantification of HLA immunogenicity using the hydrophobic mismatch score (HMS) for prospective transplants. We mined the national transplant patient database (SRTR) for a diverse group of donors and recipients with known racial backgrounds. Additionally, we use novel methods to infer survival assessment in the simulated transplants generated by our matching algorithms, in the absence of actual target outcomes, utilizing modified unsupervised clustering techniques. Our allocation algorithms demonstrated the ability to match 87.7% of Black and 86.1% of White recipients under the HLA immunogenicity threshold of 10. Notably, at the lowest HMS threshold of 0, 4.4% of Black and 12.1% of White recipients were matched, a marked increase from the 1.8% and 6.6% matched under the prevailing allocation scheme. Furthermore, our allocation algorithms yielded similar or improved survival rates, as illustrated by Kaplan-Meier (KM) curves, and enhanced survival prediction accuracy, evidenced by C-indices and Integrated Brier Scores.
Collapse
Affiliation(s)
- Fayeq Jeelani Syed
- Electrical Engineering and Computer Science Department, University of Toledo, 2801 W Bancroft St., Toledo, 43606, OH, USA
| | - Dulat Bekbolsynov
- Department of Medical Microbiology and Immunology, University of Toledo Medical Center, 3000 Arlington Ave., Toledo, 43614, OH, USA
| | - Stanislaw Stepkowski
- Department of Medical Microbiology and Immunology, University of Toledo Medical Center, 3000 Arlington Ave., Toledo, 43614, OH, USA
| | - Devinder Kaur
- Electrical Engineering and Computer Science Department, University of Toledo, 2801 W Bancroft St., Toledo, 43606, OH, USA
| | - Robert C Green
- Department of Computer Science, Bowling Green State University, 1001 E Wooster St., Bowling Green, 43403, OH, USA.
| |
Collapse
|
3
|
Pathak VK, Gangwar S, Singh R, Srivastava AK, Dikshit M. A comprehensive survey on the ant lion optimiser, variants and applications. J EXP THEOR ARTIF IN 2022. [DOI: 10.1080/0952813x.2022.2093409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Vimal Kumar Pathak
- Department of Mechanical Engineering, Manipal University Jaipur, Jaipur, India
| | - Swati Gangwar
- Department of Mechanical Engineering, Netaji Subhash University of Technology, Dwarka, India
| | - Ramanpreet Singh
- Department of Mechanical Engineering, Manipal University Jaipur, Jaipur, India
| | | | - Mithilesh Dikshit
- Department of Mechanical Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM) Ahmedabad, Ahmedabad, India
| |
Collapse
|
4
|
Gotlieb N, Azhie A, Sharma D, Spann A, Suo NJ, Tran J, Orchanian-Cheff A, Wang B, Goldenberg A, Chassé M, Cardinal H, Cohen JP, Lodi A, Dieude M, Bhat M. The promise of machine learning applications in solid organ transplantation. NPJ Digit Med 2022; 5:89. [PMID: 35817953 PMCID: PMC9273640 DOI: 10.1038/s41746-022-00637-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 06/24/2022] [Indexed: 11/16/2022] Open
Abstract
Solid-organ transplantation is a life-saving treatment for end-stage organ disease in highly selected patients. Alongside the tremendous progress in the last several decades, new challenges have emerged. The growing disparity between organ demand and supply requires optimal patient/donor selection and matching. Improvements in long-term graft and patient survival require data-driven diagnosis and management of post-transplant complications. The growing abundance of clinical, genetic, radiologic, and metabolic data in transplantation has led to increasing interest in applying machine-learning (ML) tools that can uncover hidden patterns in large datasets. ML algorithms have been applied in predictive modeling of waitlist mortality, donor–recipient matching, survival prediction, post-transplant complications diagnosis, and prediction, aiming to optimize immunosuppression and management. In this review, we provide insight into the various applications of ML in transplant medicine, why these were used to evaluate a specific clinical question, and the potential of ML to transform the care of transplant recipients. 36 articles were selected after a comprehensive search of the following databases: Ovid MEDLINE; Ovid MEDLINE Epub Ahead of Print and In-Process & Other Non-Indexed Citations; Ovid Embase; Cochrane Database of Systematic Reviews (Ovid); and Cochrane Central Register of Controlled Trials (Ovid). In summary, these studies showed that ML techniques hold great potential to improve the outcome of transplant recipients. Future work is required to improve the interpretability of these algorithms, ensure generalizability through larger-scale external validation, and establishment of infrastructure to permit clinical integration.
Collapse
Affiliation(s)
- Neta Gotlieb
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.,Department of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Amirhossein Azhie
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Divya Sharma
- Department of Gastroenterology, Toronto General Hospital Research Institute, Toronto, ON, Canada
| | - Ashley Spann
- Division of Gastroenterology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nan-Ji Suo
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada
| | - Jason Tran
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Ani Orchanian-Cheff
- Library and Information Services, University Health Network, Toronto, ON, Canada
| | - Bo Wang
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Anna Goldenberg
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Michael Chassé
- Department of Medicine (Critical Care), University of Montreal Hospital, Montréal, QC, Canada.,Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada
| | - Heloise Cardinal
- Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada.,Centre hospitalier de l'Université de Montréal Research Center, Université de Montréal, Montréal, QC, Canada
| | - Joseph Paul Cohen
- Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada.,Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA.,Mila, Quebec Artificial Intelligence Institute, Montréal, QC, Canada
| | - Andrea Lodi
- Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada.,Canada Excellence Research Chair, Polytechnique Montréal, Montréal, QC, Canada
| | - Melanie Dieude
- Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada.,Centre hospitalier de l'Université de Montréal Research Center, Université de Montréal, Montréal, QC, Canada.,Department Microbiology, Infectiology and Immunology, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada.,Héma-Québec, Montréal, QC, Canada
| | - Mamatha Bhat
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada. .,Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada. .,Division of Gastroenterology and Hepatology, Department of Medicine, University of Toronto, Toronto, ON, Canada.
| |
Collapse
|
5
|
Monga P, Sharma M, Sharma SK. A comprehensive meta-analysis of emerging swarm intelligent computing techniques and their research trend. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2021. [DOI: 10.1016/j.jksuci.2021.11.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
6
|
Chellal M, Wang J, Benmessahel I, Galoul A. Dynamic kidney paired exchange using modified multiverse optimization. EVOLUTIONARY INTELLIGENCE 2020. [DOI: 10.1007/s12065-020-00516-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
7
|
Tikhamarine Y, Malik A, Pandey K, Sammen SS, Souag-Gamane D, Heddam S, Kisi O. Monthly evapotranspiration estimation using optimal climatic parameters: efficacy of hybrid support vector regression integrated with whale optimization algorithm. ENVIRONMENTAL MONITORING AND ASSESSMENT 2020; 192:696. [PMID: 33040211 DOI: 10.1007/s10661-020-08659-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 10/05/2020] [Indexed: 06/11/2023]
Abstract
For effective planning of irrigation scheduling, water budgeting, crop simulation, and water resources management, the accurate estimation of reference evapotranspiration (ETo) is essential. In the current study, the hybrid support vector regression (SVR) coupled with Whale Optimization Algorithm (SVR-WOA) was employed to estimate the monthly ETo at Algiers and Tlemcen meteorological stations positioned in the north of Algeria under three different optimal input scenarios. Monthly climatic parameters, i.e., solar radiation (Rs), wind speed (Us), relative humidity (RH), and maximum and minimum air temperatures (Tmax and Tmin) of 14 years (2000-2013), were obtained from both stations. The accuracy of the hybrid SVR-WOA model was appraised against hybrid SVR-MVO (Multi-Verse Optimizer), and SVR-ALO (Ant Lion Optimizer) models through performance measures, i.e., mean absolute error (MAE), root-mean-square error (RMSE), index of scattering (IOS), index of agreement (IOA), Pearson correlation coefficient (PCC), Nash-Sutcliffe efficiency (NSE), and graphical interpretation (time-variation and scatter plots, radar chart, and Taylor diagram). The results showed that the SVR-WOA model performed superior to the SVR-MVO and SVR-ALO models at both stations in all scenarios. The SVR-WOA-1 model with five inputs (i.e., Tmin, Tmax, RH, Us, Rs: scenario-1) had the lowest value of MAE = 0.0658/0.0489 mm/month, RMSE = 0.0808/0.0617 mm/month, IOS = 0.0259/0.0165, and the highest value of NSE = 0.9949/0.9989, PCC = 0.9975/0.9995, and IOA = 0.9987/0.9997 for testing period at both stations, respectively. The proposed hybrid SVR-WOA model was found to be more appropriate and efficient in comparison to SVR-MVO and SVR-ALO models for estimating monthly ETo in the study region.
Collapse
Affiliation(s)
- Yazid Tikhamarine
- Department of Science and Technology, University of Tamanrasset, BP 10034 Sersouf, Tamanrasset, 11000, Algeria
- Leghyd Laboratory, Department of Civil Engineering, University of Sciences and Technology Houari Boumediene , BP 32 Al Alia, BP 32, Bab Ezzouar, Algiers, Algeria
| | - Anurag Malik
- Punjab Agricultural University, Regional Research Station, Bathinda, 151001, Punjab, India.
| | - Kusum Pandey
- Department of Soil and Water Engineering, Punjab Agricultural University, Ludhiana, 141004, Punjab, India
| | - Saad Shauket Sammen
- Department of Civil Engineering, College of Engineering, Diyala University, Baquba, Diyala 15 Governorate, Iraq
| | - Doudja Souag-Gamane
- Leghyd Laboratory, Department of Civil Engineering, University of Sciences and Technology Houari Boumediene , BP 32 Al Alia, BP 32, Bab Ezzouar, Algiers, Algeria
| | - Salim Heddam
- Faculty of Science, Agronomy Department, Hydraulics Division, University 20 Août 1955, Route EL HADAIK, BP 26, Skikda, Algeria
| | - Ozgur Kisi
- School of Technology, Ilia State University, Tbilisi, Georgia
| |
Collapse
|
8
|
Heidari AA, Faris H, Mirjalili S, Aljarah I, Mafarja M. Ant Lion Optimizer: Theory, Literature Review, and Application in Multi-layer Perceptron Neural Networks. NATURE-INSPIRED OPTIMIZERS 2020. [DOI: 10.1007/978-3-030-12127-3_3] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
|