Wong Rachel K, Pitino Michael A, Mahmood Rafid, Zhu Ian Yihang, Stone Debbie, O'Connor Deborah L, Unger Sharon, Chan Timothy C Y
Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada.
Department of Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada.
J Nutr. 2021 Jul 1;151(7):2075-2083. doi: 10.1093/jn/nxab069.
Donor milk is the standard of care for hospitalized very low birth weight (VLBW) infants when mother's milk is unavailable; however, growth of donor milk-fed infants is frequently suboptimal. Variability in nutrient composition of donated milk complicates the production of a uniform pooled product and, subsequently, the provision of adequate nutrition to promote optimal growth and development of VLBW infants. We reasoned a machine learning approach to construct batches using characteristics of the milk donation might be an effective strategy in reducing the variability in donor milk product composition.
The objective of this study was to identify whether machine learning models can accurately predict donor milk macronutrient content. We focused on predicting fat and protein, given their well-established importance in VLBW infant growth outcomes.
Samples of donor milk, consisting of 272 individual donations and 61 pool samples, were collected from the Rogers Hixon Ontario Human Milk Bank and analyzed for macronutrient content. Four different machine learning models were constructed using independent variable groups associated with donations, donors, and donor-pumping practices. A baseline model was established using lactation stage and infant gestational status. Predictions were made for individual donations and resultant pools.
Machine learning models predicted protein of individual donations and pools with a mean absolute error (MAE) of 0.16 g/dL and 0.10 g/dL, respectively. Individual donation and pooled fat predictions had an MAE of 0.91 g/dL and 0.42 g/dL, respectively. At both the individual donation and pool levels, protein predictions were significantly more accurate than baseline, whereas fat predictions were competitive with baseline.
Machine learning models can provide accurate predictions of macronutrient content in donor milk. The macronutrient content of pooled milk had a lower prediction error, reinforcing the value of pooling practices. Future research should examine how macronutrient content predictions can be used to facilitate milk bank pooling strategies.
当无法获得母亲的母乳时，捐赠母乳是住院极低出生体重（VLBW）婴儿的护理标准；然而，食用捐赠母乳的婴儿生长情况往往不理想。捐赠母乳的营养成分存在差异，这使得生产统一的混合产品变得复杂，进而影响为VLBW婴儿提供充足营养以促进其最佳生长发育。我们推测，利用捐赠母乳的特征通过机器学习方法来构建批次产品可能是减少捐赠母乳产品成分差异的有效策略。
本研究的目的是确定机器学习模型能否准确预测捐赠母乳的常量营养素含量。鉴于脂肪和蛋白质在VLBW婴儿生长结局中已明确的重要性，我们重点关注对它们的预测。
从罗杰斯·希克森安大略母乳库收集了由272份个体捐赠母乳样本和61份混合样本组成的捐赠母乳样本，并分析其常量营养素含量。使用与捐赠、捐赠者和捐赠者挤奶操作相关的自变量组构建了四种不同的机器学习模型。使用泌乳阶段和婴儿孕周情况建立了基线模型。对个体捐赠母乳样本和最终混合样本进行了预测。
机器学习模型预测个体捐赠母乳样本和混合样本中蛋白质的平均绝对误差（MAE）分别为0.16 g/dL和0.10 g/dL。个体捐赠母乳样本和混合样本中脂肪预测的MAE分别为0.91 g/dL和0.42 g/dL。在个体捐赠母乳样本和混合样本水平上，蛋白质预测均显著比基线模型更准确，而脂肪预测与基线模型相当。
机器学习模型能够准确预测捐赠母乳中的常量营养素含量。混合母乳的常量营养素含量预测误差较低，这强化了混合操作的价值。未来的研究应探讨如何利用常量营养素含量预测来促进母乳库的混合策略。
