"你好,我可以帮你什么? ":在脏研究中采用人工智能
Anita T Layton1,2,3,4
1Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada.
American journal of physiology. Renal physiology
|August 17, 2023
概括
机器学习 (ML) 为脏研究提供了强大的工具,有助于疾病诊断和预后. 本综述强调了ML在病学中的应用,旨在加快其采用以改善患者护理.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 生物医学数据科学 生物医学数据科学
- 机器学习 机器学习
背景情况:
- 大规模分子和生物医学数据集以及机器学习 (ML) 算法的进步已经改变了生物学和精准医学.
- 脏研究中的ML应用包括图像细分和电子健康记录的疾病预测,但脏学中的采用落后于其他器官系统.
- 挑战包括脏复杂性,重叠的疾病表现,资金不足,阻碍了ML的整合.
研究的目的:
- 引入机器学习概念,并审查其在脏研究中的应用.
- 讨论神经病学数据驱动预测技术的挑战和局限性.
- 鼓励脏研究界采用ML,以促进脏疾病的理解和患者护理.
主要方法:
- 审查现有的关于机器学习应用在脏生理学和病理生理学的文献.
- 在形态分析,奥米克数据解释和疾病诊断/预后中分析ML使用案例.
- 讨论在科研究中限制ML采用的因素.
主要成果:
- 机器学习在诸如脏结构细分和预测急性损伤和慢性病等疾病方面显示出显著的潜力.
- 值得注意的应用包括形态分析,奥米克数据检查以及增强疾病诊断和预后.
- 尽管有潜力,但ML在科的采用受到器官复杂性,疾病异质性和资金差异的阻碍.
结论:
- 机器学习是一个强大的工具,有可能通过提供创新的诊断和治疗解决方案来彻底改变科.
- 克服与数据复杂性,疾病表现和研究资金相关的挑战对于更广泛的ML实施至关重要.
- 提高对ML的认识和采用可以在了解脏疾病和改善患者结果方面取得重大进展.
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