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<p>A team of University of Toronto Engineering researchers, led by Professor&nbsp;<a href="https://discover.research.utoronto.ca/13303-timothy-chan/"><strong>Timothy Chan</strong></a>, is leveraging machine learning to optimize the macronutrient content of pooled human donor milk recipes.&nbsp;The researchers introduce their data-driven optimization model in a&nbsp;<a href="https://pubsonline.informs.org/doi/full/10.1287/msom.2022.0455">new paper published</a>&nbsp;in&nbsp;<em>Manufacturing and Systems Operations Management</em>.&nbsp;</p> <p>In the first phase, researchers collected the necessary data to create a machine learning model to predict the macronutrient content of the pooled recipes, and then designed an optimization model to create the recipes based on macronutrient requirements, that is, the necessary levels of protein and fat.&nbsp;The team then created a si

United statesRogers hixonSharon ungerDebbie oconnorRachel wongTimothy chanManufacturing service operations managementUniversity of toronto engineeringRogers hixon ontario human milk bankSinai healthToronto engineeringSystems operationsMount sinai hospitalDebbieo connorTemerty facultyRogers hixon ontario human milk