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Application of artificial intelligence in the management of patients with renal dysfunction.

作者信息

Zhang Bo, Jiang Xiaocong, Yang Jie, Huang Jiajie, Hu Chaoming, Hong Yucai, Ni Hongying, Zhang Zhongheng

机构信息

Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.

Department of Critical Care Medicine, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China.

出版信息

Ren Fail. 2024 Dec;46(1):2337289. doi: 10.1080/0886022X.2024.2337289. Epub 2024 Apr 3.

DOI:10.1080/0886022X.2024.2337289
PMID:38570197
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10993745/
Abstract
摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95ab/10993745/292c9b684856/IRNF_A_2337289_F0001_C.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95ab/10993745/292c9b684856/IRNF_A_2337289_F0001_C.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95ab/10993745/292c9b684856/IRNF_A_2337289_F0001_C.jpg

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本文引用的文献

1
Correlation between neutrophil-to-lymphocyte ratio and contrast-induced acute kidney injury and the establishment of machine-learning-based predictive models.中性粒细胞与淋巴细胞比值与对比剂诱导急性肾损伤的相关性及基于机器学习的预测模型的建立。
Ren Fail. 2023;45(2):2258983. doi: 10.1080/0886022X.2023.2258983. Epub 2023 Sep 27.
2
Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease.机器学习算法预测慢性肾脏病危重症患者住院死亡率。
Ren Fail. 2023 Dec;45(1):2212790. doi: 10.1080/0886022X.2023.2212790.
3
Prediction of hyperkalemia in ESRD patients by identification of multiple leads and multiple features on ECG.
通过识别心电图上的多个导联和多个特征预测 ESRD 患者的高钾血症。
Ren Fail. 2023 Dec;45(1):2212800. doi: 10.1080/0886022X.2023.2212800.
4
Bioinformatics analysis of potential pathogenesis and risk genes of immunoinflammation-promoted renal injury in severe COVID-19.生物信息学分析严重 COVID-19 中免疫炎症促进肾损伤的潜在发病机制和风险基因。
Front Immunol. 2022 Aug 16;13:950076. doi: 10.3389/fimmu.2022.950076. eCollection 2022.
5
Machine learning for early discrimination between transient and persistent acute kidney injury in critically ill patients with sepsis.机器学习在鉴别脓毒症危重症患者急性肾损伤的一过性与持续性中的作用。
Sci Rep. 2021 Oct 12;11(1):20269. doi: 10.1038/s41598-021-99840-6.
6
China Kidney Disease Network (CK-NET) 2016 Annual Data Report.中国肾脏病网（CK-NET）2016年度数据报告。
Kidney Int Suppl (2011). 2020 Dec;10(2):e97-e185. doi: 10.1016/j.kisu.2020.09.001. Epub 2020 Dec 1.
7
Artificial intelligence to guide management of acute kidney injury in the ICU: a narrative review.人工智能指导 ICU 急性肾损伤管理：叙述性综述。
Curr Opin Crit Care. 2020 Dec;26(6):563-573. doi: 10.1097/MCC.0000000000000775.
8
Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017.全球、区域和国家慢性肾脏病负担，1990-2017 年：2017 年全球疾病负担研究的系统分析。
Lancet. 2020 Feb 29;395(10225):709-733. doi: 10.1016/S0140-6736(20)30045-3. Epub 2020 Feb 13.
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Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy.机器学习在脓毒症预测中的应用：诊断试验准确性的系统评价和荟萃分析。
Intensive Care Med. 2020 Mar;46(3):383-400. doi: 10.1007/s00134-019-05872-y. Epub 2020 Jan 21.
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Perceived Health and Quality of Life in Patients With CKD, Including Those With Kidney Failure: Findings From National Surveys in France.法国全国性调查：慢性肾脏病（包括肾衰竭患者）患者的健康感知和生活质量。
Am J Kidney Dis. 2020 Jun;75(6):868-878. doi: 10.1053/j.ajkd.2019.08.026. Epub 2019 Dec 23.