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将大数据转化为用于营养和肥胖研究的AI准备数据
Diana M Thomas1, Rob Knight2, Jack A Gilbert3
1Department of Mathematical Sciences, United States Military Academy, West Point, New York, USA.
Obesity (Silver Spring, Md.)
|March 1, 2024
概括
为肥胖和营养研究预处理大数据是复杂的,需要机器学习 (ML) 和人工智能 (AI). 数据准备的透明度对于准确解释结果至关重要.
科学领域:
- 肥胖和营养研究 肥胖和营养研究
- 数据科学数据科学数据科学
- 生物信息学是一种生物信息学.
背景情况:
- 大数据为推进肥胖和营养研究提供了巨大的潜力.
- 原始大数据需要广泛的预处理,以便用于机器学习 (ML) 和人工智能 (AI) 模型.
- 预处理是最复杂的阶段,需要ML,人类判断和专业软件.
研究的目的:
- 详细说明流行肥胖/营养大数据来源的预处理管道.
- 突出在创建AI和ML准备数据方面的挑战和决策影响.
- 强调最终用户对数据预处理的理解至关重要.
主要方法:
- 审查三个主要的肥胖/营养大数据来源:微生物组,代谢学和加速计.
- 详细检查预处理管道和相关专业软件.
- 分析预处理决策如何影响最终的AI和ML准备数据产品.
主要成果:
- 确定了微生物组,代谢学和加速度计数据的特定预处理步骤,软件和挑战.
- 证明了预处理选择对AI和ML准备数据的质量和可用性的影响.
- 提出了改善质量控制,预处理速度和数据消耗的机会.
结论:
- 大数据有望在肥胖研究中发现新的可修改因素.
- 在AI和ML准备数据准备过程中的透明度对于准确的解释至关重要.
- 对于研究人员和临床医生来说,对预处理复杂性的清晰理解至关重要.
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