ArticleInternational journal of medical sciences2021
Artificial intelligence supported anemia control system (AISACS) to prevent anemia in maintenance hemodialysis patients.
Article in International journal of medical sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic review and comparison of machine learning and conventional statistical models for predicting cardiovascular events in dialysis patients.Renal failure · 2025Pooled it
- Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.Journal of nephrology · 2023Pooled it
- 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
- Article
- Leveraging ICT Tools to Improve Kidney Health: A Comprehensive Review of Innovations in Nephrology.Healthcare (Basel, Switzerland) · 2026Review
- Transforming nephrology through artificial intelligence: a state-of-the-art roadmap for clinical integration.Clinical kidney journal · 2026Review
- Anemia in young women: determinants and artificial intelligence-based management approaches.Frontiers in artificial intelligence · 2026Review
- Qualitative study on physicians' acceptance of a clinical decision support system for anemia management in patients receiving hemodialysis.BMC health services research · 2025Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases.Bioengineering (Basel, Switzerland) · 2025Review
- Article
- Anemia in Elderly Patients: Contribution of Renal Aging and Chronic Kidney Disease.Geriatrics (Basel, Switzerland) · 2025Review
- A deterministic large language model (LLM) framework for safe, protocol-adherent clinical decision support: application in hemodialysis anemia management (AnemiaCare HDs).Frontiers in artificial intelligence · 2025Article
- Optimizing anemia management using artificial intelligence for patients undergoing hemodialysis.Scientific reports · 2024Article
- Automated Dialysis Anemia Management: Role of the Treating Nephrologist.Clinical journal of the American Society of Nephrology : CJASN · 2024Article
- The Janus-faced nature of complement in hemodialysis: interplay between complement, inflammation, and bioincompatibility unveiling a self-amplifying loop contributing to organ damage.Frontiers in nephrology · 2024Article
- Proceedings of a membrane update symposium: advancements, scientific insights, and future trends for dialysis membranes for enhanced clinical outcomes in end stage kidney disease patients.Frontiers in nephrology · 2024Article
- Predicting dry weight change in Hemodialysis patients using machine learning.BMC nephrology · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anemia, for which erythropoiesis-stimulating agents (ESAs) and iron supplements (ISs) are used as preventive measures, presents important difficulties for hemodialysis patients. Nevertheless, the number of physicians able to manage such medications appropriately is not keeping pace with the rapid increase of hemodialysis patients. Moreover, the high cost of ESAs imposes heavy burdens on medical insurance systems. An artificial-intelligence-supported anemia control system (AISACS) trained using administration direction data from experienced physicians has been developed by the authors. For the system, appropriate data selection and rectification techniques play important roles. Decision making related to ESAs poses a multi-class classification problem for which a two-step classification technique is introduced. Several validations have demonstrated that AISACS exhibits high performance with correct classification rates of 72%-87% and clinically appropriate classification rates of 92%-98%.
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Registered trials
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