ArticleScientific data2019
Dataset supporting blood pressure prediction for the management of chronic hemodialysis.
Article in Scientific data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- Medical statistics education in the big data era: focusing on critical thinking and ethical competence.Frontiers in public health · 2026Article
- Predicting blood pressure variability in hemodialysis using an explainable boosting machine model.Clinical kidney journal · 2025Article
- The BestShape artificial intelligence system for real-time prediction of intradialytic hypotension-clinical outcomes after four-year follow-up.Clinical kidney journal · 2025Article
- Multilayer Perceptron-Based Real-Time Intradialytic Hypotension Prediction Using Patient Baseline Information and Heart-Rate Variation.International journal of environmental research and public health · 2022Article
- Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine.Frontiers in neuroinformatics · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hemodialysis (HD) is a treatment given to patients with renal failure. Notable treatment-related complications include hypotension, cramps, insufficient blood flow, and arrhythmia. Most complications are associated with unstable blood pressure during HD. Physicians are devoted to seeking solutions to prevent or lower the incidence of possible complications. With advances in technology, big data have been obtained in various medical fields. The accumulated dialysis records in each HD session can be gathered to obtain big HD data with the potential to assist HD staff in increasing patient wellbeing. We generated a large stream of HD parameters collected from dialysis equipment associated with the Vital Info Portal gateway and correlated with the demographic data stored in the hospital information system from each HD session. We expect that the application of HD big data will greatly assist HD staff in treating intradialytic hypotension, setting optimal dialysate parameters, and even developing an intelligent early-warning system as well as providing individualized suggestions regarding dialysis settings in the future.
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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.