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人工神经网络和深度学习在固体器官移植中

Byron H Smith¹, Aleksandar Denic²

¹Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.

Transplantation

|July 24, 2025

概括

人工神经网络 (ANN) 在固体器官移植方面提供了强大的见解,但可能是复杂的. 本指南揭开了ANN的神秘性,提供了术语和应用程序,以便在移植研究中更好地理解和使用.

科学领域:

计算机科学计算机科学
医学医学医学医学医学
生物技术是生物技术.

背景情况:

人工神经网络 (ANN) 越来越多地被用于各种科学领域,包括医学和自然语言处理.
尽管ANN的应用广泛,但通常被认为是复杂的"黑子"模型,这给解释带来了挑战.
在固体器官移植中应用ANNs的历史可以追溯到20世纪90年代.

研究的目的:

提供与人工神经网络相关的基本术语的清晰概述.
在固体器官移植领域展示ANNs的示例应用.
为有兴趣采用ANN模型的研究人员和临床医生提供指导.

主要方法:

定义和例子是通过全面的PubMed搜索来编制的.
进行了对机器学习和深度学习中已知文本的彻底审查.
专注于最近的ANN应用程序处理图像和文本等非表格数据.

主要成果:

ANN已经从处理表格数据演变为分析复杂的非表格数据类型,如医疗图像和临床笔记.
该研究概述了关键概念和术语,以促进对ANN功能更好地理解.
实际例子说明了ANN在固体器官移植的各个方面的实用性.

结论:

人工神经网络具有很大的潜力,可以促进实体器官移植的研究和实践.
了解ANN术语和应用对于有效利用其预测能力至关重要.
对ANN结果的上下文解释对于可靠的临床应用和未来的开发至关重要.

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