从诊所到计算机再回:在为儿科设计和实施机器学习解决方案时的实际考虑
Sujay Nagaraj1,2, Vinyas Harish1,3, Liam G McCoy1,3
1Faculty of Medicine, University of Toronto, Toronto, Ontario Canada.
Current treatment options in pediatrics
|April 16, 2024
概括
机器学习 (ML) 在儿科需要独特的考虑,由于发育变异和以家庭为中心的护理. 本综述概述了儿科ML项目的管道和框架,解决有效实施的伦理和数据挑战.
科学领域:
- 人工智能在医学中的应用
- 儿童机器学习研究 儿童机器学习研究
背景情况:
- 机器学习 (ML) 应用在医疗领域不断扩大,但直接转化为儿科仍受到独特的临床,技术和伦理挑战的阻碍.
- 儿科ML研究面临来自不同发育阶段的复杂性,以家庭为中心的护理模式,数据异质性和高质量的数据库稀缺性.
研究的目的:
- 为了解决在儿科应用ML的独特细微差别.
- 提供一个全面的概述,从项目构想到临床实施,在儿科中对ML的特殊考虑.
- 作为儿童医学环境中ML科学家和临床医生的指导方针.
主要方法:
- 开发一个共同的管道,用于构建儿科的ML项目.
- 建立一个框架来将ML模型转化为儿科患者的临床实践.
- 识别和讨论特定于儿科ML的伦理和法律考虑因素.
主要成果:
- 建议采用结构化的方法来应对儿科ML的复杂性.
- 审查强调需要量身定制的管道和框架来克服数据和发展挑战.
- 在整个儿科的ML项目生命周期中,强调了伦理和法律方面的考虑.
结论:
- 在儿科中实施ML需要具体的策略,考虑到发育多样性和以家庭为中心的护理.
- 拟议的管道和框架旨在促进儿童ML解决方案的成功开发和临床整合.
- 这项工作为研究人员和临床医生提供了必要的指导,以负责任和有效地推进儿科ML.
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