ArticleBiology of blood and marrow transplantation : journal of the American Society for Blood and Marrow Transplantation2018
Evaluation of a Machine Learning-Based Prognostic Model for Unrelated Hematopoietic Cell Transplantation Donor Selection.
Article in Biology of blood and marrow transplantation : journal of the American Society for Blood and Marrow Transplantation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT).Sensors (Basel, Switzerland) · 2020Pooled it
- Research on the generalization of a blood donor recruitment framework based on machine learning.BMC medical informatics and decision making · 2026Article
- The Applications of Machine Learning in the Management of Patients Undergoing Stem Cell Transplantation: Are We Ready?Cancers · 2025Review
- A gender specific risk assessment of coronary heart disease based on physical examination data.NPJ digital medicine · 2023Article
- Establishment of a predictive model for GVHD-free, relapse-free survival after allogeneic HSCT using ensemble learning.Blood advances · 2022Article
- Survival Prediction of Children Undergoing Hematopoietic Stem Cell Transplantation Using Different Machine Learning Classifiers by Performing Chi-Square Test and Hyperparameter Optimization: A Retrospective Analysis.Computational and mathematical methods in medicine · 2022Article
- Machine Learning Applications in the Diagnosis of Benign and Malignant Hematological Diseases.Clinical hematology international · 2021Review
- "Worldwide Network for Blood & Marrow Transplantation (WBMT) special article, challenges facing emerging alternate donor registries".Bone marrow transplantation · 2019Review
- Registries and artificial intelligence: investing in the future of hematopoietic cell transplantation.Bone marrow transplantation · 2019Article
- Artificial Intelligence Approaches in Hematopoietic Cell Transplantation: A Review of the Current Status and Future DirectionsTurkish journal of haematology : official journal of Turkish Society of Haematology · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 7 institutions in 3 countries.
Funding
Abstract
The survival of patients undergoing hematopoietic cell transplantation (HCT) from unrelated donors for acute leukemia exhibits considerable variation, even after stringent genetic matching. To improve the donor selection process, we attempted to create an algorithm to quantify the likelihood of survival to 5 years after unrelated donor HCT for acute leukemia, based on the clinical characteristics of the donor selected. All standard clinical variables were included in the model, which also included average leukocyte telomere length of the donor based on its association with recipient survival in severe aplastic anemia, and links to multiple malignancies. We developed a multivariate classifier that assigned a Preferred or NotPreferred label to each prospective donor based on the survival of the recipient. In a previous analysis using a resampling method, recipients with donors labeled Preferred experienced clinically compelling better survival compared with those labeled NotPreferred by the test. However, in a pivotal validation study in an independent cohort of 522 patients, the overall survival of the Preferred and NotPreferred donor groups was not significantly different. Although machine learning approaches have successfully modeled other biological phenomena and have led to accurate predictive models, our attempt to predict HCT outcomes after unrelated donor transplantation was not successful.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.