在母乳养连续性中使用机器学习模型:护理培训干预和后续研究
1Author Affiliations: Department of Pediatric Health and Diseases, Ankara Training and Research Hospital, Ankara Provincial Health Directorate, Ankara, Türkiye (Dr Yol Unlu); and Faculty of Health Sciences, Department of Nursing, Ankara Yıldırım Beyazit University, Ankara, Türkiye (Dr Kucuk).
The Journal of perinatal & neonatal nursing
|December 9, 2025
概括
基于机器学习 (ML) 的母乳养培训 (MLBT) 改善了早期戒断风险的母亲的母乳养知识和继续率. 这种方法通过个性化解决方案来提高医疗保健质量,以持续支持母乳养.
科学领域:
- 护理 护理 护理
- 公共卫生 公共卫生
- 医疗保健中的人工智能
背景情况:
- 早期停止母乳养对母亲和婴儿的健康构成风险.
- 识别有早期停止母乳养风险的母亲对于及时干预至关重要.
- 需要创新的方法来支持持续的母乳养实践.
研究的目的:
- 评估基于机器学习 (ML) 的母乳养培训 (MLBT) 对母乳养知识的影响.
- 评估MLBT对早期停止母乳养风险的母亲继续母乳养率的影响.
- 确定ML模型在识别可能提前停止母乳养的母亲的有效性.
主要方法:
- 这是一项准实验性的前后测试研究,涉及90名母亲 (45名干预者,45名对照者).
- 第一个阶段:开发一种ML模型来预测早期停止母乳养的风险.
- 第二阶段:根据确定的风险概况向干预组提供MLBT;使用千平方,t测试和ANOVA分析数据.
主要成果:
- 干预组在所有MLBT模块 (P < .01) 和总分 (P < .001) 中显示了母乳养知识的显著改善.
- 在产后2个月和4个月的干预组中,完全和部分母乳养率显著更高 (P < .005).
结论:
- 多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋多性恋
- 将ML纳入护理实践提供了高效,个性化的解决方案,以提高医疗保健质量.
- 这项研究为开发和加强促进持续母乳养的计划提供了框架,以改善母婴健康.
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