ArticleJournal of cellular and molecular medicine2025
Survival Prediction in Allogeneic Haematopoietic Stem Cell Transplant Recipients Using Pre- and Post-Transplant Factors and Computational Intelligence.
Article in Journal of cellular and molecular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Putting the I in AML: Artificial Intelligence and Machine Learning in Acute Myeloid Leukemia.Cells · 2026Review
- Computational intelligence using nailfold videocapillaroscopy for the prediction of carotid intima-media thickness in rheumatoid arthritis: a cohort-based study.Rheumatology international · 2026Article
- When the endothelium is on fire: bridging thromboinflammation and data-driven prognosis in pulmonary embolism.Research and practice in thrombosis and haemostasis · 2026Article
- Artificial intelligence-driven nano-enhanced stem cell therapy for neurodegenerative diseases: from rational design to clinical translation.Journal of nanobiotechnology · 2026Review
- Early and sex-specific dynamic changes of the modified Glasgow Prognostic Score correlate with survival and immunotoxicities in patients undergoing allogeneic hematopoietic stem cell transplantation.Frontiers in immunology · 2026Article
- Machine Learning in Stem Cell Research: From Biological Data to Clinical Translation.Computational and structural biotechnology journal · 2026Review
- 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
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Authors and funding
26 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advancements in artificial intelligence (AI) predictive models have emerged as valuable tools for predicting survival outcomes in allogeneic haematopoietic stem cell transplantation (allo-HSCT). These models primarily focus on pre-transplant factors, while algorithms incorporating changes in patient's status post-allo-HSCT are lacking. The aim of this study was to develop a predictive soft computing model assessing survival outcomes in allo-HSCT recipients. In this study, we assembled a comprehensive database comprising of 564 consecutive adult patients who underwent allo-HSCT between 2015 and 2024. Our algorithm selectively considers critical parameters from the database, ranking and evaluating them based on their impact on patient outcomes. By utilising the Data Ensemble Refinement Greedy Algorithm, we developed an AI model with 93.26% accuracy in predicting survivorship status in allo-HSCT recipients. Our model used only seven parameters, including age, disease, disease phase, creatinine levels at day 2 post-allo-HSCT, platelet engraftment, acute graft-versus-host disease (GvHD) and chronic GvHD. External validation of our AI model is considered essential. Machine learning algorithms have the potential to improve the prediction of long-term survival outcomes for patients undergoing allo-HSCT.
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Registered trials
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