ArticleInternational journal of environmental research and public health2021
Predicting the Appearance of Hypotension During Hemodialysis Sessions Using Machine Learning Classifiers.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- MI-Net: a dual-stream feature network based on Mamba-convolutional neural network (CNN) fusion to predict intradialytic hypotension using multimodal data.Quantitative imaging in medicine and surgery · 2026Article
- Continuous monitoring of relative blood volume allows real-time assessment of intradialytic hypotension risk.Clinical kidney journal · 2026Article
- Dialysis vintage modifies the effect of adsorption-based therapies on protein-bound toxin clearance.Frontiers in cell and developmental biology · 2026Article
- Prediction Model of Intradialytic Hypertension in Hemodialysis Patients Based on Machine Learning.Journal of medical systems · 2025Article
- A holistic framework for intradialytic hypotension prediction using generative adversarial networks-based data balancing.BMC medical informatics and decision making · 2025Article
- Research on the development of an intelligent prediction model for blood pressure variability during hemodialysis.BMC nephrology · 2025Article
- Construction and application of machine learning models for predicting intradialytic hypotension.PloS one · 2025Article
- Knowledge, Attitudes and Practices Toward Hypotension During Hemodialysis Among Nephrology and Hemodialysis Medical Staff: A Multi-Center Cross-Sectional Study.Journal of multidisciplinary healthcare · 2025Article
- Review
- Analyzing Monthly Blood Test Data to Forecast 30-Day Hospital Readmissions among Maintenance Hemodialysis Patients.Journal of clinical medicine · 2024Article
- Development of a clinical automatic calculation of hypoglycemia during hemodialysis risk in patients with diabetic nephropathy.Diabetology & metabolic syndrome · 2023Article
- Construction of an Early Alert System for Intradialytic Hypotension before Initiating Hemodialysis Based on Machine Learning.Kidney diseases (Basel, Switzerland) · 2023Article
- Predicting dry weight change in Hemodialysis patients using machine learning.BMC nephrology · 2023Article
- Model for Predicting Complications of Hemodialysis Patients Using Data From the Internet of Medical Things and Electronic Medical Records.IEEE journal of translational engineering in health and medicine · 2023Article
- Intraoperative prediction of postanaesthesia care unit hypotension.British journal of anaesthesia · 2022Article
- Deep Learning Model for Predicting Intradialytic Hypotension Without Privacy Infringement: A Retrospective Two-Center Study.Frontiers in medicine · 2022Article
- Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
A patient suffering from advanced chronic renal disease undergoes several dialysis sessions on different dates. Several clinical parameters are monitored during the different hours of any of these sessions. These parameters, together with the information provided by other parameters of analytical nature, can be very useful to determine the probability that a patient may suffer from hypotension during the session, which should be specially watched since it represents a proven factor of possible mortality. However, the analytical information is not always available to the healthcare personnel, or it is far in time, so the clinical parameters monitored during the session become key to the prevention of hypotension. This article presents an investigation to predict the appearance of hypotension during a dialysis session, using predictive models trained from a large dialysis database, which contains the clinical information of 98,015 sessions corresponding to 758 patients. The prediction model takes into account up to 22 clinical parameters measured five times during the session, as well as the gender and age of the patient. This model was trained by means of machine learning classifiers, providing a success in the prediction higher than 80%.
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