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논문 기본 정보

자료유형
학술저널
저자정보
Cheungpasitporn Wisit (Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA) Thongprayoon Charat (Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA) Kashani Kianoush B. (Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA)
저널정보
대한신장학회 Kidney Research and Clinical Practice Kidney Research and Clinical Practice Vol.43 No.4
발행연도
2024.7
수록면
417 - 432 (16page)
DOI
10.23876/j.krcp.23.298

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Sepsis-associated acute kidney injury (SA-AKI) is a serious complication in critically ill patients, resulting in higher mortality, morbidity, and cost. The intricate pathophysiology of SA-AKI requires vigilant clinical monitoring and appropriate, prompt intervention. While traditional statistical analyses have identified severe risk factors for SA-AKI, the results have been inconsistent across studies. This has led to growing interest in leveraging artificial intelligence (AI) and machine learning (ML) to predict SA-AKI better. ML can uncover complex patterns beyond human discernment by analyzing vast datasets. Supervised learning models like XGBoost and RNN-LSTM have proven remarkably accurate at predicting SA-AKI onset and subsequent mortality, often surpassing traditional risk scores. Meanwhile, unsupervised learning reveals clinically relevant sub-phenotypes among diverse SA-AKI patients, enabling more tailored care. In addition, it potentially optimizes sepsis treatment to prevent SA-AKI through continual refinement based on patient outcomes. However, utilizing AI/ML presents ethical and practical challenges regarding data privacy, algorithmic biases, and regulatory compliance. AI/ML allows early risk detection, personalized management, optimal treatment strategies, and collaborative learning for SA-AKI management. Future directions include real-time patient monitoring, simulated data generation, and predictive algorithms for timely interventions. However, a smooth transition to clinical practice demands continuous model enhancements and rigorous regulatory oversight. In this article, we outlined the conventional methods used to address SA-AKI and explore how AI and ML can be applied to diagnose and manage SA-AKI, highlighting their potential to revolutionize SA-AKI care.

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