ArticleJCO clinical cancer informatics2020
Predicting Acute Graft-Versus-Host Disease Using Machine Learning and Longitudinal Vital Sign Data From Electronic Health Records.
Article in JCO clinical cancer informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Putting the I in AML: Artificial Intelligence and Machine Learning in Acute Myeloid Leukemia.Cells · 2026Review
- Article
- 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
- Twenty-Five Years of Evolution and Hurdles in Electronic Health Records and Interoperability in Medical Research: Comprehensive Review.Journal of medical Internet research · 2025Review
- Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care.JMIR mHealth and uHealth · 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
- Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study.BMC infectious diseases · 2024Observational
- Article
- 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
- A convolutional neural network-based model that predicts acute graft-versus-host disease after allogeneic hematopoietic stem cell transplantation.Communications medicine · 2023Article
- Elaborating the potential of Artificial Intelligence in automated CAR-T cell manufacturing.Frontiers in molecular medicine · 2023Article
- Assessment of risk factors for acute graft-Therapeutic advances in hematology · 2023Article
- Article
- Leukemia can be Effectively Early Predicted in Routine Physical Examination with the Assistance of Machine Learning Models.Journal of healthcare engineering · 2022Article
- Use of Automated Thematic Annotations for Small Data Sets in a Psychotherapeutic Context: Systematic Review of Machine Learning Algorithms.JMIR mental health · 2021Review
- Predicting postoperative opioid use with machine learning and insurance claims in opioid-naïve patients.American journal of surgery · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAcute graft-versus-host disease (aGVHD) remains a significant complication of allogeneic hematopoietic cell transplantation (HCT) and limits its broader application. The ability to predict grade II to IV aGVHD could potentially mitigate morbidity and mortality. To date, researchers have focused on using snapshots of a patient (eg, biomarkers at a single time point) to predict aGVHD onset. We hypothesized that longitudinal data collected and stored in electronic health records (EHRs) could distinguish patients at high risk of developing aGVHD from those at low risk. PATIENTS AND
methodsThe study included a cohort of 324 patients undergoing allogeneic HCT at the University of Michigan C.S. Mott Children's Hospital during 2014 to 2017. Using EHR data, specifically vital sign measurements collected within the first 10 days of transplantation, we built a predictive model using penalized logistic regression for identifying patients at risk for grade II to IV aGVHD. We compared the proposed model with a baseline model trained only on patient and donor characteristics collected at the time of transplantation and performed an analysis of the importance of different input features.
resultsThe proposed model outperformed the baseline model, with an area under the receiver operating characteristic curve of 0.659 versus 0.512 (
conclusionLeveraging readily available clinical data from EHRs, we developed a machine-learning model for aGVHD prediction in patients undergoing HCT. Continuous monitoring of vital signs, such as temperature, could potentially help clinicians more accurately identify patients at high risk for aGVHD.
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