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机器学习在慢性病中的应用:现状和未来前景

Charlotte Delrue¹, Sander De Bruyne², Marijn M Speeckaert^1,3

¹Department of Nephrology, Ghent University Hospital, 9000 Ghent, Belgium.

|March 28, 2024

概括

人工智能和机器学习 (ML) 为改善慢性病 (CKD) 护理提供了新的途径. 在病学中应用ML可以增强数据解释,以便更好地诊断和个性化医疗.

科学领域:

腎臟病學 (nephrology) 是一種醫學專業.
人工智能的人工智能
机器学习机器学习

背景情况:

人工智能和机器学习 (ML) 正在改变临床医学.
这些技术为推进医疗实践和研究提供了巨大的潜力,特别是在治疗慢性疾病方面.
慢性病 (CKD) 呈现出一个复杂的数据景观,为ML驱动的洞察力而成熟.

研究的目的:

审查ML在慢性病 (CKD) 领域的当前应用和未来潜力.
探索ML如何增强脏病学中的数据解释和统计建模.
强调将ML集成到临床算法中的重要性,以提高效率和采用.

主要方法:

这篇叙述性综述综合了CKD中ML应用的当前文献.
它检查了统计和计算机科学在从广泛的数据集中获得洞察力的交叉点.
该评论讨论了将ML集成到临床算法和数据解释策略中.

主要成果:

机器学习使计算机能够从大型医疗数据集中获得有意义的见解.
在脏病学中应用ML有望改善数据解释和统计建模.
将ML集成到临床算法中可以提高效率并帮助采用新的数据解释标准.

结论:

机器学习对推进CKD的精确诊断和个性化医学有很大的前景.
临床医生和数据科学家之间的合作对于制定有效的数据共享和使用政策至关重要.
随着ML的不断发展,它将进一步加强其在改善CKD患者护理和研究方面的作用.

关键词:

在IgA脏病发作中,IgA脏病发作慢性脏疾病慢性脏疾病糖尿病性脏病糖尿病性脏病血液透析是血液透析的方法之一.机器学习是机器学习.

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