ArticleJAMIA open2021
Machine learning-based prediction of health outcomes in pediatric organ transplantation recipients.
Article in JAMIA open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 21 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
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Machine learning-based techniques to improve lung transplantation outcomes and complications: a systematic review.BMC medical research methodology · 2022Pooled it
- Growth and development following paediatric kidney transplantation: Mechanisms, influencing factors, and clinical management.World journal of transplantation · 2026Review
- Growth Trajectories of Medication Adherence in Pediatric Organ Transplantation and Their Relationship to Posttransplant Health Outcomes.Transplantation direct · 2026Article
- Predicting Organ Rejections for Pediatric Heart Transplantations with a Combined Use of Transplant Registry Data and Electronic Health Records.medRxiv : the preprint server for health sciences · 2025Article
- Predicting graft survival in paediatric kidney transplant recipients using machine learning.Pediatric nephrology (Berlin, Germany) · 2025Article
- Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era.PloS one · 2025Article
- Changes in Medication Adherence Across the Posttransplant Period in Pediatric Organ Transplant Recipients.Clinical transplantation · 2024Article
- Artificial Intelligence in Pediatric Liver Transplantation: Opportunities and Challenges of a New Era.Children (Basel, Switzerland) · 2024Review
- Demographic Factors, Medication Adherence, and Post-transplant Health Outcomes: A Longitudinal Multilevel Modeling Approach.Journal of clinical psychology in medical settings · 2024Article
- Prediction of Outcomes After Heart Transplantation in Pediatric Patients Using National Registry Data: Evaluation of Machine Learning Approaches.JMIR cardio · 2023Article
- Assessment of Performance, Interpretability, and Explainability in Artificial Intelligence-Based Health Technologies: What Healthcare Stakeholders Need to Know.Mayo Clinic proceedings. Digital health · 2023Review
- Differences between Very Highly Sensitized Kidney Transplant Recipients as Identified by Machine Learning Consensus Clustering.Medicina (Kaunas, Lithuania) · 2023Article
- Distinct Phenotypes of Non-Citizen Kidney Transplant Recipients in the United States by Machine Learning Consensus Clustering.Medicines (Basel, Switzerland) · 2023Article
- Distinct clinical profiles and post-transplant outcomes among kidney transplant recipients with lower education levels: uncovering patterns through machine learning clustering.Renal failure · 2023Article
- The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review.Frontiers in public health · 2023Article
- Distinct phenotypes of kidney transplant recipients aged 80 years or older in the USA by machine learning consensus clustering.BMJ surgery, interventions, & health technologies · 2023Article
- Digital Technologies: Advancing Individualized Treatments through Gene and Cell Therapies, Pharmacogenetics, and Disease Detection and Diagnostics.Biomedicines · 2022Article
- Pre-operative Machine Learning for Heart Transplant Patients Bridged with Temporary Mechanical Circulatory Support.Journal of cardiovascular development and disease · 2022Article
- Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States.Journal of clinical medicine · 2022Article
- Distinct Phenotypes of Kidney Transplant Recipients in the United States with Limited Functional Status as Identified through Machine Learning Consensus Clustering.Journal of personalized medicine · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesPrediction of post-transplant health outcomes and identification of key factors remain important issues for pediatric transplant teams and researchers. Outcomes research has generally relied on general linear modeling or similar techniques offering limited predictive validity. Thus far, data-driven modeling and machine learning (ML) approaches have had limited application and success in pediatric transplant outcomes research. The purpose of the current study was to examine ML models predicting post-transplant hospitalization in a sample of pediatric kidney, liver, and heart transplant recipients from a large solid organ transplant program. MATERIALS AND
methodsVarious logistic regression, naive Bayes, support vector machine, and deep learning (DL) methods were used to predict 1-, 3-, and 5-year post-transplant hospitalization using patient and administrative data from a large pediatric organ transplant center.
resultsDL models generally outperformed traditional ML models across organtypes and prediction windows with area under the receiver operating characteristic curve values ranging from 0.750 to 0.851. Shapley additive explanations (SHAP) were used to increase the interpretability of DL model results. Various medical, patient, and social variables were identified as salient predictors across organ types. DISCUSSION: Results demonstrate the utility of DL modeling for health outcome prediction with pediatric patients, and its use represents an important development in the prediction of post-transplant outcomes in pediatric transplantation compared to prior research.
conclusionResults point to DL models as potentially useful tools in decision-support systems assisting physicians and transplant teams in identifying patients at a greater risk for poor post-transplant outcomes.
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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.