新生儿和幼儿的功能性胃肠道疾病预测因素:一种机器学习方法来评估风险
Flavia Indrio1, Elio Masciari2, Flavia Marchese3
1Department of Experimental Medicine School of Medicine University of Salento, Lecce, Italy.
Heliyon
|January 21, 2025
概括
研究人员确定了婴儿功能性胃肠道疾病 (FGID) 的早期生命风险因素. 出生的体重,带血pH值和母亲的年龄是关键预测因素,可为FGIDs提供早期识别和定制的预防策略.
科学领域:
- 儿科 儿科 儿科
- 胃肠病学 胃肠病学
- 人工智能在医学中的应用
背景情况:
- 功能性胃肠道疾病 (FGIDs) 对儿童,家庭和医疗保健系统构成重大负担.
- FGIDs的确切病理生理学仍然在很大程度上是未知的,这阻碍了早期识别有风险的婴儿.
- 这项研究的重点在于在生命的第一年内确定FGIDs的早期生命风险因素.
研究的目的:
- 为了确定婴儿功能性胃肠道疾病 (FGIDs) 的早期生命风险因素.
- 开发基于人工智能的预测模型,用于FGID风险评估.
- 为临床医生创建一个实用的,基于网络的工具,以识别有风险的婴儿.
主要方法:
- 预期观察队列研究期满和早产婴儿 (n=6060).
- 使用传统的统计方法和随机森林分类模型 (AI).
- 确定了与生命第一年FGID发展相关的关键风险因素.
主要成果:
- 人工智能识别出出生体重,带血pH值和母亲的年龄是FGID的重要预测因素.
- 通过传统统计和人工智能识别的风险因素之间存在差异.
- 人工智能模型突出了对于早期FGID风险预测至关重要的特定变量.
结论:
- 机器学习首次确定了出生体重,带血pH值和母亲年龄作为预测婴儿FGID的关键变量.
- 开发的基于人工智能的风险评估工具可以帮助临床医生识别可能受益于预防性干预的婴儿.
- 这种方法有助于为患有FGIDs风险的婴儿制定量身定制的预防战略.
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