目的 探究人工智能(artificial intelligence,AI)应用于血液透析领域的研究现状与发展趋势。 方法 检索建库至2023年3月29日Web of Science核心合集数据库中科学引文索引扩展版和社会科学引文索引收录的AI应用于血液透析领域的文献，应用文献计量学的研究方法与VOSviewer软件对国家、研究机构、期刊、作者、关键词等进行可视化分析及视图展现。 结果 共有98篇论著和5篇综述纳入分析。近20年，AI应用于血液透析领域研究产出整体呈上升趋势，发文量居首位的国家是中国(n=37)，发文量最多的机构是费森尤斯医疗(n=16)。本领域目前及未来的前沿研究趋势为血液透析结局、影响因素以及并发症预测模型。 结论 使用文献计量学与VOSviewer软件进行研究分析，可直观地展现领域内的研究现状和前沿热点，为今后进一步的研究提供参考依据。
Objective To investigate the current research status and development trend of artificial intelligence (AI) applied to hemodialysis. Methods We searched the literature in the field of AI applied to hemodialysis indexed by SCI-EXPANDED and SSCI in the Web of Science Core Collection database from the establishment of the database to March 29, 2023. We applied the bibliometric research method and VOSviewer software to visualize and present the countries, research institutions, journals, authors, and keywords. Results A total of 98 articles and 5 reviews were enrolled in the analysis. In the past 20 years, the application of AI to the field of hemodialysis has shown an overall and rising trend. The country with the most publications was China (n=37), and the institution with the most publications was Fresenius Medical Care (n=16). Hemodialysis outcomes, influencing factors, and complication prediction models are the current and future frontier of research trends in this field. Conclusion Using bibliometrics and VOSviewer software for analysis can visualize the current status of research and cutting-edge hotspots in the field and provide a reference basis for further research in the future.
[1] Bello A K, Levin A, Lunney M, et al. Global Kidney Health Atlas: a report by the International Society of Nephrology on the global burden of end-stage kidney disease and capacity for kidney replacement therapy and conservative care across world countries and regions [J]. Brussels: International Society of Nephrology, 2019.
[2] 中国医药教育协会肾病与血液净化专业委员会血液透析低血压防治专家组. 血液透析中低血压防治专家共识(2022) [J]. 中华内科杂志, 2022, 61(3): 269-281.
[3] 张冬, 龚德华. 人工智能在血液透析中的应用 [J]. 肾脏病与透析肾移植杂志, 2018, 27(04): 383-386.
[4] Hamet P, Tremblay J. Artificial intelligence in medicine [J]. Metabolism, 2017, 69: S36-S40.
[5] Shin H, Choi B H, Shim O, et al. Single test-based diagnosis of multiple cancer types using Exosome-SERS-AI for early stage cancers [J]. Nat Commun, 2023, 14(1): 1644.
[6] Zheng C, Lee M, Bansal N, et al. Identification of Recurrent Atrial Fibrillation using Natural Language Processing Applied to Electronic Health Records [J]. Eur Heart J Qual Care Clin Outcomes, 2023.
[7] Wu S, Hong G, Xu A, et al. Artificial intelligence-based model for lymph node metastases detection on whole slide images in bladder cancer: a retrospective, multicentre, diagnostic study [J]. The Lancet Oncology, 2023.
[8] Junaid M, Ali S, Eid F, et al. Explainable Machine Learning Models based on Multimodal Time-Series Data for the Early Detection of Parkinson’s Disease [J]. Computer Methods and Programs in Biomedicine, 2023: 107495.
[9] 涂嘉欣, 叶惠清, 张小强, 等. 2000—2022年人工智能应用于食管癌领域全球研究的可视化分析 [J]. 中国全科医学, 2023, 26(06): 760-768.
[10] Van Eck N, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping [J]. scientometrics, 2010, 84(2): 523-538.
[11] 宋秀芳, 迟培娟. Vosviewer与Citespace应用比较研究 [J]. 情报科学, 2016, 34: 108-12+46.
[12] Barbieri C, Molina M, Ponce P, et al. An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients [J]. Kidney international, 2016, 90(2): 422-429.
[13] Lin C J, Chen C Y, Wu P C, et al. Intelligent system to predict intradialytic hypotension in chronic hemodialysis [J]. Journal of the Formosan Medical Association, 2018, 117(10): 888-893.
[14] Lee H, Yun D, Yoo J, et al. Deep Learning Model for Real-Time Prediction of Intradialytic Hypotension [J]. Clin J Am Soc Nephrol, 2021, 16(3): 396-406.
[15] Chen T, Guestrin C. Xgboost: A scalable tree boosting system [C]. proceedings of the Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, F, 2016.
[16] Stefánsson B V, Brunelli S M, Cabrera C, et al. Intradialytic hypotension and risk of cardiovascular disease [J]. Clinical journal of the American Society of Nephrology, 2014, 9(12): 2124-2132.
[17] Foundation N K. Kidney Early Evaluation Program [J]. American journal of kidney diseases: the official journal of the National Kidney Foundation, 2005, 45(2): S1-135.
[18] Chiu J S, Chong C F, Lin Y F, et al. Applying an artificial neural network to predict total body water in hemodialysis patients [J]. American journal of nephrology, 2005, 25(5): 507-513.
[19] Barbieri C, Cattinelli I, Neri L, et al. Development of an artificial intelligence model to guide the management of blood pressure, fluid volume, and dialysis dose in end-stage kidney disease patients: proof of concept and first clinical assessment [J]. Kidney diseases, 2019, 5(1): 28-33.
[20] Akl A I, Sobh M A, Enab Y M, et al. Artificial intelligence: a new approach for prescription and monitoring of hemodialysis therapy [J]. American journal of kidney diseases, 2001, 38(6): 1277-1283.
[21] Escandell-Montero P, Chermisi M, Martinez-Martinez J M, et al. Optimization of anemia treatment in hemodialysis patients via reinforcement learning [J]. Artificial intelligence in medicine, 2014, 62(1): 47-60.
[22] Price D J. Little science, big science... and beyond [M]. Columbia University Press New York, 1986.
[23] 张丽, 邢晨, 景筠. 重症肌无力生活质量研究的现状和热点分析—基于Web of Science数据库的文献计量学分析 [J]. 中国神经免疫学和神经病学杂志, 2023, 30(01): 39-45.
[24] Bellocchio F, Garbelli M, Apel C, et al. MO801: Use of the Anemia Control Model is Associated With Improved Hemoglobin Target Achievement, as Well as Lower Rates of Inappropriate ESA USE And Severe Anemia Among Dialysis Patients [J]. Nephrology Dialysis Transplantation, 2022, 37(Supplement_3): gfac081. 06.
[25] Park J H, Yoon J, Park I, et al. A deep learning algorithm to quantify AVF stenosis and predict 6-month primary patency: a pilot study [J]. Clin Kidney J, 2023, 16(3): 560-570.
