Donor Selection for Hematopoietic Stem Cell Transplant Using Cost-Sensitive SVM

Sivasankaran, Adarsh; Cherkassky, Vladimir; Albrecht, Mark; Williams, Eric; Maiers, Martin

doi:10.1109/icmla.2015.166

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…More methods have been proposed to develop algorithms that can help in the selection of donor-recipient pairs. For instance, two abstracts [ 19 , 20 ] proposed the use of different ML tools to aid this process. Sarkar and Srivastava [ 19 ] developed an algorithm that used both HLA and killer-cell immunoglobulin-like receptor to improve the selection of donors for recipients with acute myelogenous leukemia (AML).…”

Section: Resultsmentioning

confidence: 99%

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Artificial Intelligence approaches in hematopoietic cell transplant: A review of the current status and future directions

Muhsen

Elhassan

Hashmi

2018

Tjh

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The evidence-based literature on healthcare is currently expanding exponentially. The opportunities provided by the advancement in artificial intelligence (AI) tools such as machine learning are appealing in tackling many of the current healthcare challenges. Thus, AI integration is expanding in most fields of healthcare, including the field of hematology. This study aims to review the current applications of AI in the field of hematopoietic cell transplantation (HCT). A literature search was done involving the following databases: Ovid MEDLINE, including In-Process and other non-indexed citations, and Google Scholar. The abstracts of the following professional societies were also screened: American Society of Hematology, American Society for Blood and Marrow Transplantation, and European Society for Blood and Marrow Transplantation. The literature review showed that the integration of AI in the field of HCT has grown remarkably in the last decade and offers promising avenues in diagnosis and prognosis in HCT populations targeting both pre- and post-transplant challenges. Studies of AI integration in HCT have many limitations that include poorly tested algorithms, lack of generalizability, and limited use of different AI tools. Machine learning techniques in HCT are an intense area of research that needs much development and extensive support from hematology and HCT societies and organizations globally as we believe that this will be the future practice paradigm.

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Section: Resultsmentioning

confidence: 99%

“…The algorithm was able to increase the accuracy of predictions by 3%-4% compared to the usual analysis. Sivasankaran et al [ 20 ] proposed a black-box model in developing a system that uses secondary non-HLA characteristics in selecting donors, though no data on the validation or improvement of accuracy have been reported to date.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence approaches in hematopoietic cell transplant: A review of the current status and future directions

Muhsen

Elhassan

Hashmi

2018

Tjh

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“…Moreover, determining the availability of unrelated donors is also a key concern. Therefore, research has been done to automate the donor selection and availability tasks using ML techniques [35][36][37][38]. A recent study by Li et al [35] proposed an ML approach to predict donors' availability by considering five years of donor information as input variables and responses to verification typing (VT) requests as outcome variables.…”

Section: Pre-transplant Factorsmentioning

confidence: 99%

“…For donor selection algorithms, some studies [37,38] utilized SVM for building a predictive model to identify appropriate unrelated donors. Buturovic et al [37] developed a model to prioritize donors as preferred or not preferred based on the five-year survival status of their recipient, while Sivasankaran et al [38] developed a model to select optimal HLA-matched donors based on donor characteristics and historical choice behavior. However, the authors in [37] were unsuccessful in predicting the unrelated donor.…”

Section: Pre-transplant Factorsmentioning

confidence: 99%

A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT)

Gupta

Braun

Chowdhury

et al. 2020

Sensors

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Machine learning techniques are widely used nowadays in the healthcare domain for the diagnosis, prognosis, and treatment of diseases. These techniques have applications in the field of hematopoietic cell transplantation (HCT), which is a potentially curative therapy for hematological malignancies. Herein, a systematic review of the application of machine learning (ML) techniques in the HCT setting was conducted. We examined the type of data streams included, specific ML techniques used, and type of clinical outcomes measured. A systematic review of English articles using PubMed, Scopus, Web of Science, and IEEE Xplore databases was performed. Search terms included “hematopoietic cell transplantation (HCT),” “autologous HCT,” “allogeneic HCT,” “machine learning,” and “artificial intelligence.” Only full-text studies reported between January 2015 and July 2020 were included. Data were extracted by two authors using predefined data fields. Following PRISMA guidelines, a total of 242 studies were identified, of which 27 studies met the inclusion criteria. These studies were sub-categorized into three broad topics and the type of ML techniques used included ensemble learning (63%), regression (44%), Bayesian learning (30%), and support vector machine (30%). The majority of studies examined models to predict HCT outcomes (e.g., survival, relapse, graft-versus-host disease). Clinical and genetic data were the most commonly used predictors in the modeling process. Overall, this review provided a systematic review of ML techniques applied in the context of HCT. The evidence is not sufficiently robust to determine the optimal ML technique to use in the HCT setting and/or what minimal data variables are required.

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“…We use data from a total of 592 searches for model training. General experimental setup is as followed in (Sivasankaran et al 2016), which has more detailed considerations of problem formalization, misclassification costs, multiple performance measurements, and model selection criteria used in this modelling effort. A number of publicly available packages have SVM implementation.…”

Section: Donor Chosenmentioning

confidence: 99%

Unrelated Donor Selection for Stem Cell Transplants using Predictive Modelling

Sivasankaran

Williams²,

Maiers³

et al. 2018

Preprint

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Unrelated Donor selection for a Hematopoietic Stem Cell Transplant is a complex multi-stage process. Choosing the most suitable donor from a list of Human Leukocyte Antigen (HLA) matched donors can be challenging to even the most experienced physicians and search coordinators. The process involves experts sifting through potentially thousands of genetically compatible donors based on multiple factors. We propose a Machine Learning approach to donor selection based on historical searches performed and selections made for these searches. We describe the process of building a computational model to mimic the donor selection decision process and show benefits of using the proposed model in this study.

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Donor Selection for Hematopoietic Stem Cell Transplant Using Cost-Sensitive SVM

Cited by 5 publications

References 11 publications

Artificial Intelligence approaches in hematopoietic cell transplant: A review of the current status and future directions

Artificial Intelligence approaches in hematopoietic cell transplant: A review of the current status and future directions

A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT)

Unrelated Donor Selection for Stem Cell Transplants using Predictive Modelling

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