Machine Learning Applications in Nephrology: A Bibliometric Analysis Comparing Kidney Studies to Other Medicine Subspecialities.
Machine Learning Applications in Nephrology: A Bibliometric Analysis Comparing Kidney Studies to Other Medicine Subspecialities.
复制标题
机器学习在肾脏病学中的应用：比较肾脏研究与其他医学亚专业的文献计量分析。
DOI：
10.1016/j.xkme.2021.04.012
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发表时间：
2021-09
期刊：
影响因子：
3.9
通讯作者：
Kolachalama VB
中科院分区：
文献类型：
--
作者：
Verma A;Chitalia VC;Waikar SS;Kolachalama VB
由机器学习算法驱动的人工智能正越来越多地用于早期检测、疾病诊断和临床管理。我们探索了机器学习驱动的进步在肾脏研究中的应用，与其他器官特定领域相比。横截面文献计量分析。使用关于器官系统、期刊国际标准序列号和研究方法的特定医学主题词（MeSH）查询ISI Web of Science数据库。与此同时，我们筛选了美国国立卫生研究院（NIH）RePORTER网站，以探索提出使用机器学习作为方法的资助赠款。使用机器学习作为研究方法的出版物数量。文章通过5个器官系统（脑、心脏、肾脏、肝脏和肺）的研究方法进行表征。由NIH资助的机器学习赠款的特点是研究部分。在5个器官系统中比较了使用机器学习和其他研究方法的文章数量。关注肾脏的基于机器学习的文章占5个器官系统相关文章总数的3.2%。具体来说，大脑研究发表的文章数量比肾脏研究高出19倍。与机器学习相比，传统的统计方法，如考克斯比例风险模型，在与肾脏研究相关的文章中的使用率高出9倍。一般来说，在特定器官的专业期刊中观察到的基于机器学习的方法的利用率低于广泛的跨学科期刊。消化疾病、肾脏和泌尿科研究部门资助了122项基于机器学习方法的应用，而神经学、神经心理学和神经病理学研究部门资助了265项应用。观察性研究。我们的分析表明，与其他器官特定的研究人员相比，肾脏研究人员使用机器学习作为研究工具的比例最低，这强调了需要更好地向肾脏研究界介绍这种新兴的数据分析工具。
Artificial intelligence driven by machine learning algorithms is being increasingly employed for early detection, disease diagnosis, and clinical management. We explored the use of machine learning–driven advancements in kidney research compared with other organ-specific fields. Cross-sectional bibliometric analysis. ISI Web of Science database was queried using specific Medical Subject Headings (MeSH) terms about the organ system, journal International Standard Serial Number, and research methodology. In parallel, we screened the National Institutes of Health (NIH) RePORTER website to explore funded grants that proposed the use of machine learning as a methodology. Number of publications using machine learning as a research method. Articles were characterized by research methodology among 5 organ systems (brain, heart, kidney, liver, and lung). Grants funded by NIH for machine learning were characterized by study sections. Percentages of articles using machine learning and other research methodologies were compared among 5 organ systems. Machine learning-based articles that are focused on the kidney accounted for 3.2% of the total relevant articles from the 5 organ systems. Specifically, brain research published over 19-fold higher number of articles than kidney research. As compared with machine learning, conventional statistical approaches such as the Cox proportional hazard model were used 9-fold higher in articles related to kidney research. In general, a lower utilization of machine learning–based approaches was observed in organ-specific specialty journals than the broad interdisciplinary journals. The digestive disease, kidney, and urology study sections funded 122 applications proposing machine learning–based approaches compared to 265 applications from the neurology, neuropsychology, and neuropathology study sections. Observational study. Our analysis suggests lowest use of machine learning as a research tool among kidney researchers compared with other organ-specific researchers, underscoring a need to better inform the kidney research community about this emerging data analytic tool.
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DOI：
10.2215/cjn.04260415
发表时间：
2015-11-01
影响因子：
9.8
作者：
Denker, Matthew;Boyle, Suzanne;Feldman, Harold I.
通讯作者：
Feldman, Harold I.
影响因子：
64.8
作者：
Tomasev, Nenad;Glorot, Xavier;Mohamed, Shakir
通讯作者：
Mohamed, Shakir
DOI：
10.1038/s41581-020-00335-w
发表时间：
2020-11
期刊：
Nature reviews. Nephrology
影响因子：
--
作者：
Ong E;Wang LL;Schaub J;O'Toole JF;Steck B;Rosenberg AZ;Dowd F;Hansen J;Barisoni L;Jain S;de Boer IH;Valerius MT;Waikar SS;Park C;Crawford DC;Alexandrov T;Anderton CR;Stoeckert C;Weng C;Diehl AD;Mungall CJ;Haendel M;Robinson PN;Himmelfarb J;Iyengar R;Kretzler M;Mooney S;He Y;Kidney Precision Medicine Project
通讯作者：
Kidney Precision Medicine Project
影响因子：
13.2
作者：
Niel, Olivier;Bastard, Paul
通讯作者：
Bastard, Paul
影响因子：
13.6
作者：
Ginley, Brandon;Lutnick, Brendon;Sarder, Pinaki
通讯作者：
Sarder, Pinaki