Predicting Protein and Fat Content in Human Donor Milk Using Machine Learning. Issue 7 (13th April 2021)

• Record Type: Journal Article
• Authors: Wong, Rachel K; Pitino, Michael A; Mahmood, Rafid; Zhu, Ian Yihang; Stone, Debbie; O'Connor, Deborah L; Unger, Sharon; Chan, Timothy C Y
• Is Part Of: Journal of nutrition. Volume 151:Issue 7(2021)
• Page Start: 2075
• Page End: 2083
• Publication Date: 2021-04-13
• Subjects: donor human milk -- human milk banking -- macronutrient analysis -- macronutrient prediction -- machine learning; Nutrition -- Periodicals; Diet -- Periodicals; 613.205
• DOI: 10.1093/jn/nxab069
• Languages: English
• ISSNs: 0022-3166

Abstract

ABSTRACT: Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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.