ReviewBone marrow transplantation2014
Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT.
Review in Bone marrow transplantation, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 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
48 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The predictive value of machine learning for mortality risk in patients with acute coronary syndromes: a systematic review and meta-analysis.European journal of medical research · 2023Pooled it
- Predictive models in extracorporeal membrane oxygenation (ECMO): a systematic review.Systematic reviews · 2023Pooled it
- A novel Iowa-Mayo validated composite risk assessment tool for allogeneic stem cell transplantation survival outcome prediction.Blood cancer journal · 2021Trial
- Application of Machine Learning Techniques for Clinical Predictive Modeling: A Cross-Sectional Study on Nonalcoholic Fatty Liver Disease in China.BioMed research international · 2018Trial
- A holistic prognostic model for leukemia-free survival after allogeneic transplantation in acute leukemia.Bone marrow transplantation · 2026Article
- Integrating large language models for enhanced predictive analytics in healthcare.NPJ digital medicine · 2026Article
- Harnessing network pharmacology in drug discovery: an integrated approach.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Machine learning-based preoperative prediction of perioperative venous thromboembolism in Chinese lung cancer patients: a retrospective cohort study.Frontiers in oncology · 2025Article
- Establishment of a nomogram model based on immune-related genes using machine learning for aortic dissection diagnosis and immunomodulation assessment.International journal of medical sciences · 2025Article
- Development and evaluation of machine learning models for predicting large-for-gestational-age newborns in women exposed to radiation prior to pregnancy.BMC medical informatics and decision making · 2024Article
- Predicting pressure gradient using artificial intelligence for transcatheter aortic valve replacement.JTCVS techniques · 2024Article
- Prediction of longitudinal clinical outcomes after acute myocardial infarction using a dynamic machine learning algorithm.Frontiers in cardiovascular medicine · 2024Article
- Advancements in Umbilical Cord Biobanking: A Comprehensive Review of Current Trends and Future Prospects.Stem cells and cloning : advances and applications · 2024Review
- Unsupervised meta-clustering identifies risk clusters in acute myeloid leukemia based on clinical and genetic profiles.Communications medicine · 2023Article
- Application of supervised machine learning algorithms for classification and prediction of type-2 diabetes disease status in Afar regional state, Northeastern Ethiopia 2021.Scientific reports · 2023Article
- Prediction of permanent pacemaker implantation after transcatheter aortic valve replacement: The role of machine learning.World journal of cardiology · 2023Article
- Big Data in Gastroenterology Research.International journal of molecular sciences · 2023Review
- Article
- Establishment of a predictive model for GVHD-free, relapse-free survival after allogeneic HSCT using ensemble learning.Blood advances · 2022Article
- Development and Evaluation of a Machine Learning Prediction Model for Small-for-Gestational-Age Births in Women Exposed to Radiation before Pregnancy.Journal of personalized medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data collected from hematopoietic SCT (HSCT) centers are becoming more abundant and complex owing to the formation of organized registries and incorporation of biological data. Typically, conventional statistical methods are used for the development of outcome prediction models and risk scores. However, these analyses carry inherent properties limiting their ability to cope with large data sets with multiple variables and samples. Machine learning (ML), a field stemming from artificial intelligence, is part of a wider approach for data analysis termed data mining (DM). It enables prediction in complex data scenarios, familiar to practitioners and researchers. Technological and commercial applications are all around us, gradually entering clinical research. In the following review, we would like to expose hematologists and stem cell transplanters to the concepts, clinical applications, strengths and limitations of such methods and discuss current research in HSCT. The aim of this review is to encourage utilization of the ML and DM techniques in the field of HSCT, including prediction of transplantation outcome and donor selection.
Indexed as
Identifiers
24096823What 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.