Edge computing with federated learning for early detection of citric acid overdose and adjustment of regional citrate anticoagulation
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| Publicado en: | BMC Medical Informatics and Decision Making vol. 25 (2025), p. 1-23 |
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| Otros Autores: | , , |
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Springer Nature B.V.
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1186/s12911-025-03130-4 |2 doi | |
| 035 | |a 3247098286 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 58451 |2 nlm | ||
| 100 | 1 | |a Mali, Saroj | |
| 245 | 1 | |a Edge computing with federated learning for early detection of citric acid overdose and adjustment of regional citrate anticoagulation | |
| 260 | |b Springer Nature B.V. |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Regional citrate anticoagulation (RCA) is critical for extracorporeal anticoagulation in continuous renal replacement therapy done at the bedside. To make patients’ data more secure and to help with computer-based monitoring of dosages, we suggest a system that uses machine learning. This system will give early alerts about citric acid overdose and advise changes to how much citrate and calcium gluconate are infused into the patient’s body. Citric acid overdose causes significant clinical risks, emphasizing the need for better adaptable anticoagulation procedures that can respond quickly. The study puts forward a new structure that uses edge computing and federated learning to make better citrate anticoagulation procedures. We proposed the resource-aware Federated Learning with Dynamic Client Selection (RAFL-Fed) algorithm in our method. In this setup, every client takes part by training a local model locally and then sending its outcome to a main server. The algorithm chooses clients for each training session depending on their computing resources, which keeps things efficient and scalable. The server collects the client inputs using weighted averages to update the global model. This step is performed repeatedly across many communication cycles, letting the system adjust to changing data trends from different locations. We put RAFL-Fed to the test on the MIMIC-IV dataset, and it outperformed other methods, getting a high accuracy of 0.9615 (IID) and 0.9571 (Non-IID), also with the lowest loss values being 0.2625 and 0.2469 in that order. It also noted the best MAE at 0.1731 (Non-IID) and a bit higher at 0.2081 (IID). Along with the high sensitivity at 0.9968, specificity stood strong as well, measuring 0.9449, plus latency was only 0.123s, which shows how effective it is for early detection of citric acid overdose as well as adjusting in real-time in the regional citrate anticoagulation process. The proposed method shows a promising solution for the real-time monitoring and adjustment of citrate anticoagulation regimens, greatly enhancing patient data security and treatment effectiveness in clinical settings. This method signifies a significant advancement in handling anticoagulation therapy. | |
| 653 | |a Anticoagulants | ||
| 653 | |a Acids | ||
| 653 | |a Algorithms | ||
| 653 | |a Telemedicine | ||
| 653 | |a Edge computing | ||
| 653 | |a Data processing | ||
| 653 | |a Privacy | ||
| 653 | |a Monitoring | ||
| 653 | |a Machine learning | ||
| 653 | |a Citric acid | ||
| 653 | |a Overdose | ||
| 653 | |a Patients | ||
| 653 | |a Electronic health records | ||
| 653 | |a Data integrity | ||
| 653 | |a Electrolytes | ||
| 653 | |a Servers | ||
| 653 | |a Response rates | ||
| 653 | |a Effectiveness | ||
| 653 | |a Hospitals | ||
| 653 | |a Latency | ||
| 653 | |a Kidneys | ||
| 653 | |a Real time | ||
| 653 | |a Federated learning | ||
| 653 | |a Intensive care | ||
| 653 | |a Renal replacement therapy | ||
| 653 | |a Drug overdose | ||
| 700 | 1 | |a Mali, Niroj | |
| 700 | 1 | |a Zeng, Feng | |
| 700 | 1 | |a Zhang, Ling | |
| 773 | 0 | |t BMC Medical Informatics and Decision Making |g vol. 25 (2025), p. 1-23 | |
| 786 | 0 | |d ProQuest |t Healthcare Administration Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3247098286/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3247098286/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3247098286/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |