ArticleBlood advances2019
Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation.
Article in Blood advances, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
37 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning in paediatric haematological malignancies: a systematic review of prognosis, toxicity and treatment response models.Pediatric research · 2025Pooled it
- A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT).Sensors (Basel, Switzerland) · 2020Pooled it
- Autonomous artificial intelligence prescribing a drug to prevent severe acute graft-versus-host disease in HLA-haploidentical transplants.Nature communications · 2025Trial
- The optimal conditioning intensity of stem cell transplantation for acute myeloid leukemia in complete remission.Annals of hematology · 2026Article
- Explainable Survival Modeling in Pediatric Hematopoietic Stem Cell Transplantation Using Landmark-Based Machine Learning.Scientifica · 2026Article
- Addressing the translation gap in SCT donor selection.Frontiers in immunology · 2026Review
- Machine learning-based prediction of G-CSF-induced hematopoietic stem cell mobilization outcomes in healthy volunteers.Bone marrow transplantation · 2025Article
- Application of Artificial Intelligence in Inborn Errors of Immunity Identification and Management: Past, Present, and Future-A Systematic Review.Journal of clinical medicine · 2025Review
- Article
- Microbiome-based prediction of allogeneic hematopoietic stem cell transplantation outcome.Genome medicine · 2025Article
- Development of an ensemble prediction model for acute graft-versus-host disease in allogeneic transplantation based on machine learning.BMC medical informatics and decision making · 2025Article
- Ocular graft-versus-host disease: Risk factors of ocular graft-versus-host disease after allogeneic haematopoietic stem cell transplantation in Denmark.Acta ophthalmologica · 2025Review
- The Applications of Machine Learning in the Management of Patients Undergoing Stem Cell Transplantation: Are We Ready?Cancers · 2025Review
- Modern-Era Challenges in the Clinical Management of Graft-Versus-Host Disease.Advances in experimental medicine and biology · 2025Review
- High-dimensional Immune Profiles and Machine Learning May Predict Acute Myeloid Leukemia Relapse Early following Transplant.Journal of immunology (Baltimore, Md. : 1950) · 2024Article
- Embracing the age of artificial intelligence: paradigm shifts, opportunities, and challenges in the treatment of acute graft-versus-host disease after allogeneic hematopoietic stem cell transplantation.Science China. Life sciences · 2024Article
- Leveraging machine learning for predicting acute graft-versus-host disease grades in allogeneic hematopoietic cell transplantation for T-cell prolymphocytic leukaemia.BMC medical research methodology · 2024Article
- Longitudinal clinical data improve survival prediction after hematopoietic cell transplantation using machine learning.Blood advances · 2024Article
- A dynamic time-to-event model for prediction of acute graft-versus-host disease in patients after allogeneic hematopoietic stem cell transplantation.Cancer medicine · 2024Article
- Using T-lymphocyte subsets at engraftment to predict the risk of acute graft-versus-host disease in patients with thalassemia major: development of a new predictive nomogram.Therapeutic advances in hematology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute graft-versus-host disease (aGVHD) is 1 of the critical complications that often occurs following allogeneic hematopoietic stem cell transplantation (HSCT). Thus far, various types of prediction scores have been created using statistical calculations. The primary objective of this study was to establish and validate the machine learning-dependent index for predicting aGVHD. This was a retrospective cohort study that involved analyzing databases of adult HSCT patients in Japan. The alternating decision tree (ADTree) machine learning algorithm was applied to develop models using the training cohort (70%). The ADTree algorithm was confirmed using the hazard model on data from the validation cohort (30%). Data from 26 695 HSCT patients transplanted from allogeneic donors between 1992 and 2016 were included in this study. The cumulative incidence of aGVHD was 42.8%. Of >40 variables considered, 15 were adapted into a model for aGVHD prediction. The model was tested in the validation cohort, and the incidence of aGVHD was clearly stratified according to the categorized ADTree scores; the cumulative incidence of aGVHD was 29.0% for low risk and 58.7% for high risk (hazard ratio, 2.57). Predicting scores for aGVHD also demonstrated the link between the risk of development aGVHD and overall survival after HSCT. The machine learning algorithms produced clinically reasonable and robust risk stratification scores. The relatively high reproducibility and low impacts from the interactions among the variables indicate that the ADTree algorithm, along with the other data-mining approaches, may provide tools for establishing risk score.
Indexed as
Identifiers
What OpenQuestion holds
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.