ArticleJournal of medical Internet research2021
Machine Learning Analysis of Time-Dependent Features for Predicting Adverse Events During Hemodialysis Therapy: Model Development and Validation Study.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 21 citations in OpenAlex.
- From assistant to collaborator: A systematic review of the evolution of artificial intelligence in end-stage renal disease care and management.PLOS digital health · 2026Article
- 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
- Clinical Artificial Intelligence Agents in Nephrology: From Prediction to Action Through Workflow-Native Intelligence-A Roadmap for Workflow-Integrated Care.Journal of clinical medicine · 2026Review
- Hemodialysis equipment malfunction incidence and risk profiling in clinical settings: a retrospective single-center cohort analysis.Biomedical engineering online · 2025Article
- The BestShape artificial intelligence system for real-time prediction of intradialytic hypotension-clinical outcomes after four-year follow-up.Clinical kidney journal · 2025Article
- Single-Cell RNA Sequencing Integrated with Bulk-RNA Sequencing Analysis Reveals Prognostic Signatures Based on PANoptosis in Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2025Article
- IRIME: Mitigating exploitation-exploration imbalance in RIME optimization for feature selection.iScience · 2024Article
- Data-driven, two-stage machine learning algorithm-based prediction scheme for assessing 1-year and 3-year mortality risk in chronic hemodialysis patients.Scientific reports · 2023Observational
- Explainable Machine Learning Techniques To Predict Amiodarone-Induced Thyroid Dysfunction Risk: Multicenter, Retrospective Study With External Validation.Journal of medical Internet research · 2023Article
- Early prediction of hemodialysis complications employing ensemble techniques.Biomedical engineering online · 2022Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHemodialysis (HD) therapy is an indispensable tool used in critical care management. Patients undergoing HD are at risk for intradialytic adverse events, ranging from muscle cramps to cardiac arrest. So far, there is no effective HD device-integrated algorithm to assist medical staff in response to these adverse events a step earlier during HD.
objectiveWe aimed to develop machine learning algorithms to predict intradialytic adverse events in an unbiased manner.
methodsThree-month dialysis and physiological time-series data were collected from all patients who underwent maintenance HD therapy at a tertiary care referral center. Dialysis data were collected automatically by HD devices, and physiological data were recorded by medical staff. Intradialytic adverse events were documented by medical staff according to patient complaints. Features extracted from the time series data sets by linear and differential analyses were used for machine learning to predict adverse events during HD.
resultsTime series dialysis data were collected during the 4-hour HD session in 108 patients who underwent maintenance HD therapy. There were a total of 4221 HD sessions, 406 of which involved at least one intradialytic adverse event. Models were built by classification algorithms and evaluated by four-fold cross-validation. The developed algorithm predicted overall intradialytic adverse events, with an area under the curve (AUC) of 0.83, sensitivity of 0.53, and specificity of 0.96. The algorithm also predicted muscle cramps, with an AUC of 0.85, and blood pressure elevation, with an AUC of 0.93. In addition, the model built based on ultrafiltration-unrelated features predicted all types of adverse events, with an AUC of 0.81, indicating that ultrafiltration-unrelated factors also contribute to the onset of adverse events.
conclusionsOur results demonstrated that algorithms combining linear and differential analyses with two-class classification machine learning can predict intradialytic adverse events in quasi-real time with high AUCs. Such a methodology implemented with local cloud computation and real-time optimization by personalized HD data could warn clinicians to take timely actions in advance.
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