SynthesisSensors (Basel, Switzerland)2020
A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT).
Synthesis in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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
25 citing papers in PubMed.
- The BIOPREVENT machine-learning algorithm predicts chronic graft-versus-host disease and mortality risk using posttransplant biomarkers.The Journal of clinical investigation · 2026Trial
- Research on the generalization of a blood donor recruitment framework based on machine learning.BMC medical informatics and decision making · 2026Article
- From thresholds to trajectories: a perspective on reframing alloimmune risk for computational modeling in solid organ transplantation.Frontiers in immunology · 2026Review
- AI-driven prognostics in pediatric bone marrow transplantation: a CAD approach with Bayesian and PSO optimization.BMC medical informatics and decision making · 2025Article
- Blood proteomics for quantitative biomarkers of cellular therapies.Biomarker research · 2025Review
- Survival Prediction in Allogeneic Haematopoietic Stem Cell Transplant Recipients Using Pre- and Post-Transplant Factors and Computational Intelligence.Journal of cellular and molecular medicine · 2025Article
- Article
- Machine learning-based prediction of nitrogen-fixing efficiency in Cowpea rhizobia from the Brazilian semiarid.World journal of microbiology & biotechnology · 2025Article
- Survival risk prediction in hematopoietic stem cell transplantation for multiple myeloma.Journal of integrative bioinformatics · 2025Article
- The Applications of Machine Learning in the Management of Patients Undergoing Stem Cell Transplantation: Are We Ready?Cancers · 2025Review
- Gut microbiota and graft-versus-host disease in hematopoietic stem cell transplant patients.Iranian journal of microbiology · 2024Article
- Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care.JMIR mHealth and uHealth · 2024Article
- A literature review: machine learning-based stem cell investigation.Annals of translational medicine · 2024Review
- Brave new world: expanding home care in stem cell transplantation and advanced therapies with new technologies.Frontiers in immunology · 2024Review
- How to organise a datathon for bridging between data science and healthcare? Insights from the Technion-Rambam machine learning in healthcare datathon event.BMJ health & care informatics · 2023Article
- Children's Oncology Group's 2023 blueprint for research: Cellular therapy and stem cell transplantation.Pediatric blood & cancer · 2023Article
- Review
- Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews.International journal of medical informatics · 2022Review
- Application of Machine Learning Methods for Epilepsy Risk Ranking in Patients with Hematopoietic Malignancies Using.Journal of personalized medicine · 2022Article
- Gaps and Opportunities of Artificial Intelligence Applications for Pediatric Oncology in European Research: A Systematic Review of Reviews and a Bibliometric Analysis.Frontiers in oncology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
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.
Indexed as
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What Socratic 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 Socratic graph.