Got (Optimal) Milk? Pooling Donations in Human Milk Banks with Machine Learning and Optimization

Published Online:https://doi.org/10.1287/msom.2022.0455

References

  • Alpaydm E (1999) Combined 5× 2 cv F test for comparing supervised classification learning algorithms. Neural Comput. 11(8):1885–1892.CrossrefGoogle Scholar
  • Ayer T, Zhang C, Zeng C, White CC III, Joseph VR (2019) Analysis and improvement of blood collection operations. Manufacturing Service Oper. Management 21(1):29–46.LinkGoogle Scholar
  • Ballard O, Morrow AL (2013) Human milk composition: Nutrients and bioactive factors. Pediatric Clinics 60(1):49–74.Google Scholar
  • Ban GY, El Karoui N, Lim AE (2018) Machine learning and portfolio optimization. Management Sci. 64(3):1136–1154.LinkGoogle Scholar
  • Ban GY, Gallien J, Mersereau AJ (2019) Dynamic procurement of new products with covariate information: The residual tree method. Manufacturing Service Oper. Management 21(4):798–815.LinkGoogle Scholar
  • Bartlett PL, Mendelson S (2002) Rademacher and Gaussian complexities: Risk bounds and structural results. J. Machine Learn. Res. 3(Nov):463–482.Google Scholar
  • Ben-Tal A, El Ghaoui L, Nemirovski A (2009) Robust Optimization, Princeton Series in Applied Mathematics, vol. 28 (Princeton University Press, Princeton, NJ).CrossrefGoogle Scholar
  • Bertsimas D, Copenhaver MS (2018) Characterization of the equivalence of robustification and regularization in linear and matrix regression. Eur. J. Oper. Res. 270(3):931–942.CrossrefGoogle Scholar
  • Bertsimas D, Kallus N (2020) From predictive to prescriptive analytics. Management Sci. 66(3):1025–1044.LinkGoogle Scholar
  • Bertsimas D, Gupta V, Kallus N (2018) Data-driven robust optimization. Math. Programming 167(2):235–292.CrossrefGoogle Scholar
  • Bertsimas D, O’Hair A, Relyea S, Silberholz J (2016) An analytics approach to designing combination chemotherapy regimens for cancer. Management Sci. 62(5):1511–1531.LinkGoogle Scholar
  • Boutilier JJ, Chan TC (2020) Ambulance emergency response optimization in developing countries. Oper. Res. 68(5):1315–1334.LinkGoogle Scholar
  • Cascante-Bonilla P, Tan F, Qi Y, Ordonez V (2021) Curriculum labeling: Revisiting pseudo-labeling for semi-supervised learning. Proc. AAAI Conf. Artif. Intell. 35(8):6912–6920.Google Scholar
  • Chen Y, Maravelias CT (2022) Variable bound tightening and valid constraints for multiperiod blending. INFORMS J. Comput. 34(4):2073–2090.LinkGoogle Scholar
  • Copenhaver MS, Hu M, Levi R, Safavi K, Zenteno Langle AC (2019) Health system innovation: Analytics in action. INFORMS Tutorials Oper. Res. 238–266.Google Scholar
  • Cormontagne D, Rigourd V, Vidic J, Rizzotto F, Bille E, Ramarao N (2021) Bacillus cereus induces severe infections in preterm neonates: Implication at the hospital and human milk bank level. Toxins (Basel) 13(2):123.CrossrefGoogle Scholar
  • Dai X, Wang X, He R, Du W, Zhong W, Zhao L, Qian F (2020) Data-driven robust optimization for crude oil blending under uncertainty. Comput. Chem. Engrg. 136(2):106595.CrossrefGoogle Scholar
  • de Halleux V, Rigo J (2013) Variability in human milk composition: Benefit of individualized fortification in very-low-birth-weight infants. Amer. J. Clin. Nutr. 98(2):529S–535S.CrossrefGoogle Scholar
  • DeSalvo G, Mohri M (2016) Random composite forests. Proc. AAAI Conf. Artif. Intell. 30(1):1540–1546.Google Scholar
  • DeWitt CW, Lasdon LS, Waren AD, Brenner DA, Melhem SA (1989) Omega: An improved gasoline blending system for Texaco. Interfaces 19(1):85–101.LinkGoogle Scholar
  • Elmachtoub AN, Grigas P (2022) Smart “predict, then optimize.” Management Sci. 68(1):9–26.LinkGoogle Scholar
  • Ferreira KJ, Lee BHA, Simchi-Levi D (2016) Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing Service Oper. Management 18(1):69–88.LinkGoogle Scholar
  • Gidrewicz DA, Fenton TR (2014) A systematic review and meta-analysis of the nutrient content of preterm and term breast milk. BMC Pediatr. 14(1):1–14.CrossrefGoogle Scholar
  • Glen J (1988) A mixed integer programming model for fertiliser policy evaluation. Eur. J. Oper. Res. 35(2):165–171.CrossrefGoogle Scholar
  • Hay WW, Thureen P (2010) Protein for preterm infants: How much is needed? How much is enough? How much is too much? Pediatr. Neonatol. 51(4):198–207.CrossrefGoogle Scholar
  • Hopperton KE, Pitino MA, Chouinard-Watkins R, Shama S, Sammut N, Bando N, Williams BA, et al. (2021) Determinants of fatty acid content and composition of human milk fed to infants born weighing < 1250 g. Amer. J. Clin. Nutr. 114(4):1523–1534.CrossrefGoogle Scholar
  • Jain S, Jónasson JO, Pauphilet J, Ramdas K (2022) Robust combination testing: Methods and application to covid-19 detection. Preprint, submitted January 25, https://dx.doi.org/10.2139/ssrn.4012658.Google Scholar
  • John A, Sun R, Maillart L, Schaefer A, Hamilton Spence E, Perrin MT (2019) Macronutrient variability in human milk from donors to a milk bank: Implications for feeding preterm infants. PLoS One 14(1):e0210610.CrossrefGoogle Scholar
  • Karmarkar US, Rajaram K (2001) Grade selection and blending to optimize cost and quality. Oper. Res. 49(2):271–280.LinkGoogle Scholar
  • Kim J, Unger S (2010) Human milk banking. Paediatr. Child Health 15(9):595–598.CrossrefGoogle Scholar
  • Liu S, He L, Max Shen ZJ (2021) On-time last-mile delivery: Order assignment with travel-time predictors. Management Sci. 67(7):4095–4119.LinkGoogle Scholar
  • Long J, Jiang S, He R, Zhao L (2021) Diesel blending under property uncertainty: A data-driven robust optimization approach. Fuel 306:121647.CrossrefGoogle Scholar
  • McDowall D, McCleary R, Bartos BJ (2019) Interrupted Time Series Analysis (Oxford University Press, Oxford, UK).CrossrefGoogle Scholar
  • McIntosh C, Conroy L, Tjong MC, Craig T, Bayley A, Catton C, Gospodarowicz M, et al. (2021) Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer. Nature Medicine 27(6):999–1005.CrossrefGoogle Scholar
  • Medina IMF, Fernández-Sola C, López-Rodríguez MM, Hernández-Padilla JM, Jiménez Lasserrotte MDM, Granero-Molina J (2019) Barriers to providing mother’s own milk to extremely preterm infants in the NICU. Adv. Neonatal Care 19(5):349–360.CrossrefGoogle Scholar
  • Meinzen-Derr J, Poindexter B, Wrage L, Morrow A, Stoll B, Donovan E (2009) Role of human milk in extremely low birth weight infants’ risk of necrotizing enterocolitis or death. J. Perinatol. 29(1):57–62.CrossrefGoogle Scholar
  • Mišić VV, Perakis G (2020) Data analytics in operations management: A review. Manufacturing Service Oper. Management 22(1):158–169.LinkGoogle Scholar
  • O’Connor DL, Gibbins S, Kiss A, Bando N, Brennan-Donnan J, Ng E, Campbell DM, et al. (2016) Effect of supplemental donor human milk compared with preterm formula on neurodevelopment of very low-birth-weight infants at 18 months: A randomized clinical trial. J. Amer. Medical Assoc. 316(18):1897–1905.CrossrefGoogle Scholar
  • Papageorgiou DJ, Toriello A, Nemhauser GL, Savelsbergh MW (2012) Fixed-charge transportation with product blending. Transportation Sci. 46(2):281–295.LinkGoogle Scholar
  • Paredes-Belmar G, Marianov V, Bronfman A, Obreque C, Lüer-Villagra A (2016) A milk collection problem with blending. Transportation Res. Part E Logist. Transportation Rev. 94:26–43.CrossrefGoogle Scholar
  • Paredes-Belmar G, Montero E, Lüer-Villagra A, Marianov V, Araya-Sassi C (2022) Vehicle routing for milk collection with gradual blending: A case arising in Chile. Eur. J. Oper. Res. 303(3):1403–1416.CrossrefGoogle Scholar
  • Penfold RB, Zhang F (2013) Use of interrupted time series analysis in evaluating healthcare quality improvements. Acad. Pediatr. 13(6):S38–S44.CrossrefGoogle Scholar
  • Ramey SR, Merlino Barr S, Moore KA, Groh-Wargo S (2021) Exploring innovations in human milk analysis in the neonatal intensive care unit: A survey of the United States. Frontiers Nutr. 8:692600.CrossrefGoogle Scholar
  • Redko I, Morvant E, Habrard A, Sebban M, Bennani Y (2020) A survey on domain adaptation theory: Learning bounds and theoretical guarantees. Preprint, submitted April 24, https://arxiv.org/abs/2004.11829.Google Scholar
  • Rochow N, Landau-Crangle E, Fusch C (2015) Challenges in breast milk fortification for preterm infants. Curr. Opin. Clin. Nutr. Metab. Care 18(3):276–284.CrossrefGoogle Scholar
  • Schaffer AL, Dobbins TA, Pearson SA (2021) Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: A guide for evaluating large-scale health interventions. BMC Med. Res. Methodol. 21(1):1–12.CrossrefGoogle Scholar
  • Settles B (2009) Active learning literature survey. Technical report, University of Wisconsin-Madison Department of Computer Sciences, Madison, WI.Google Scholar
  • Shi P, Helm JE, Deglise-Hawkinson J, Pan J (2021) Timing it right: Balancing inpatient congestion vs. readmission risk at discharge. Oper. Res. 69(6):1842–1865.LinkGoogle Scholar
  • Sun R, Maillart LM, Valeva S, Schaefer AJ, Starks S (2022) Optimal pooling, batching, and pasteurizing of donor human milk. Service Sci. 14(1):13–34.LinkGoogle Scholar
  • Updegrove K (2013) Nonprofit human milk banking in the United States. J. Midwifery Womens Health 58(5):502–508.CrossrefGoogle Scholar
  • Walani SR (2020) Global burden of preterm birth. Internat. J. Gynaecol. Obstet. 150(1):31–33.CrossrefGoogle Scholar
  • Wong RK, Pitino MA, Mahmood R, Zhu IY, Stone D, O’Connor DL, Unger S, Chan TC (2021) Predicting protein and fat content in human donor milk using machine learning. J. Nutrition 151(7):2075–2083.Google Scholar
  • Xu H, Mannor S (2012) Robustness and generalization. Machine Learn. 86(3):391–423.CrossrefGoogle Scholar
  • Yang Y, Vayanos P, Barton PI (2017) Chance-constrained optimization for refinery blend planning under uncertainty. Indust. Engrg. Chem. Res. 56(42):12139–12150.CrossrefGoogle Scholar
  • Young BE, Murphy K, Borman LL, Heinrich R, Krebs NF (2020) Milk bank pooling practices impact concentrations and variability of bioactive components of donor human milk. Frontiers Nutr. 7:579115.CrossrefGoogle Scholar
  • Zenteno AC, Carnes T, Levi R, Daily BJ, Dunn PF (2016) Systematic or block allocation at a large academic medical center. Ann. Surg. 264(6):973–981.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.