The Strategy of Blood Supply and Demand Management and The Application of Artificial Intelligence Models in Emergencies
DOI:
https://doi.org/10.46701/jbbt260508Keywords:
Emergencies, Blood transfusion, Blood supply and demand, Blood inventory management, Artificial intelligence modelsAbstract
As critical and unique emergency supplies, blood resources are characterized by time sensitivity and demanding preservation requirements. In recent years, emergencies such as natural disasters, conflicts, and epidemics have become increasingly frequent worldwide. These events have not only caused widespread social and economic disruption but have also destabilized the blood supply and caused persistent and severe blood shortages. When an emergency occurs, blood services must assess needs and plan actions proactively. Artificial intelligence models can capture higher-order relationships between covariates and outcomes in large datasets and play an important role in addressing blood supply and demand challenges. The use of appropriate artificial intelligence (AI) models to predict and configure the demand and supply of clinical blood products is therefore essential for assisting blood service planning in emergencies. However, a comprehensive review of the application value of AI models in emergency strategies for blood supply and demand management remains lacking. This work summarizes commonly used prediction models for blood demand, strategies for blood supply and demand management in emergencies, and model applications, with the goal of helping hospitals and blood centers select effective response strategies through different predictive models to maximize the protection of patients' lives and health. This review clarifies emergency blood management strategies for different types of emergencies, outlines core selection criteria for AI models, recommends time series models for linear and stationary data, and machine learning models for complex non-linear data, and offers practical guidance for emergency blood supply assurance.
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[1] Nieto-Calvache AJ, Quintero-Santacruz M, Macia-Mejia C, et al. Dangerous shortage of blood banks as an indirect effect of SARS-CoV-2: An obstetrics perspective. Int J Gynaecol Obstet. 2020;151（3）:424−430. DOI: 10.1002/ijgo.13409.
[2] Abdella Y, Hajjeh R, Sibinga CTS. Availability and safety of blood transfusion during humanitarian emergencies. East Mediterr Health J. 2018;24（8）:778−788. DOI: 10.26719/2018.24.8.778.
[3] Shih H, Rajendran S. Comparison of Time Series Methods and Machine Learning Algorithms for Forecasting Taiwan Blood Services Foundation's Blood Supply. J Healthc Eng. 2019;2019:6123745. DOI: 10.1155/2019/6123745.
[4] Li N, Arnold DM, Down DG, et al. From demand forecasting to inventory ordering decisions for red blood cells through integrating machine learning, statistical modeling, and inventory optimization. Transfusion. 2022;62（1）:87−99. DOI: 10.1111/trf.16739.
[5] Al-Riyami AZ, Abdella YE, Badawi MA, et al. The impact of COVID-19 pandemic on blood supplies and transfusion services in Eastern Mediterranean Region. Transfus Clin Biol. 2021;28（1）:16−24. DOI: 10.1016/j.tracli.2020.11.002.
[6] Bian Y, Chang TMS. A novel nanobiotherapeutic poly-[hemoglobin-superoxide dismutase-catalase-carbonic anhydrase] with no cardiac toxicity for the resuscitation of a rat model with 90 minutes of sustained severe hemorrhagic shock with loss of 2/3 blood volume. Artif Cells Nanomed Biotechnol. 2015;43（1）:1−9. DOI: 10.3109/21691401.2014.964554.
[7] Shan H, Zhang P. Viral attacks on the blood supply: the impact of severe acute respiratory syndrome in Beijing. Transfusion. 2004;44（4）:467−469. DOI: 10.1111/j.0041-1132.2004.04401.x.
[8] Kuruppu KKS. Management of blood system in disasters. Biologicals. 2010;38（1）:87−90. DOI: 10.1016/j.biologicals.2009.10.005.
[9] Fortis SP, Kriebardis AG, Georgatzakou HT, et al. Economic crisis in Greece: The invisible enemy of blood donation or not. Transfus Apher Sci. 2022;61（6）:103467. DOI: 10.1016/j.transci.2022.103467.
[10] Feng Y, Xu Z, Sun X, et al. Machine learning for predicting preoperative red blood cell demand. Transfus Med. 2021;31（4）:262−270. DOI: 10.1111/tme.12794.
[11] Abujaber A, Fadlalla A, Gammoh D, et al. Prediction of in-hospital mortality in patients on mechanical ventilation post traumatic brain injury: machine learning approach. BMC Med Inform Decis Mak. 2020;20（1）:336. DOI: 10.1186/s12911-020-01363-z.
[12] Mitterecker A, Hofmann A, Trentino KM, et al. Machine learning-based prediction of transfusion. Transfusion. 2020;60（9）:1977−1986. DOI: 10.1111/trf.15935.
[13] Liu LP, Zhao QY, Wu J, et al. Machine Learning for the Prediction of Red Blood Cell Transfusion in Patients During or After Liver Transplantation Surgery. Front Med （Lausanne）. 2021;8:632210. DOI: 10.3389/fmed.2021.632210.
[14] Choi EY, Choi SH, Lee H. Development and Evaluation of a Screening Scale for Indirect Trauma Caused by Media Exposure to Social Disasters. Int J Environ Res Public Health. 2021;18（2）:698. DOI: 10.3390/ijerph18020698.
[15] Doughty H, Strandenes G. Whole blood in disaster and major incident planning. ISBT Sci Ser. 2019;14（3）:323−331. DOI: 10.1111/voxs.12503.
[16] Birnbaum ML, Daily EK, O'Rourke AP, et al. Research and Evaluations of the Health Aspects of Disasters, Part I: An Overview. Prehosp Disaster Med. 2015;30（5）:512−522. DOI: 10.1017/s1049023x15005129.
[17] Irisawa A. The 2011 Great East Japan Earthquake: A Report of a Regional Hospital in Fukushima Prefecture Coping with the Fukushima Nuclear Disaster. Dig Endosc. 2012;24（s1）:3−7. DOI: 10.1111/j.1443-1661.2012.01280.x.
[18] Gschwender A, Gillard L. Disaster preparedness in the blood bank. Clin Lab Sci. 2017;30（4）:250−257. DOI: 10.29074/ascls.30.4.250.
[19] Goel R, Zhu X, Patel EU, et al. Blood transfusion trends in the United States: national inpatient sample, 2015 to 2018. Blood Adv. 2021;5（20）:4179−4184. DOI: 10.1182/bloodadvances.2021005361.
[20] Gilcher RO. Two Oklahoma City Disasters: The Oklahoma City Bombing and the Tornadoes-A Blood Bank Perspective. Proc （Bayl Univ Med Cent）. 2001;14（2）:140−143. DOI: 10.1080/08998280.2001.11927750.
[21] Tsubokura M, Nakada H, Matsumura T, et al. The impact of H1N1 influenza A virus pandemic on the blood donations in Hyogo Prefecture, Japan. Transfusion. 2010;50（8）:1803−1805. DOI: 10.1111/j.1537-2995.2010.02641.x.
[22] Gehrie EA, Frank SM, Goobie SM. Balancing Supply and Demand for Blood during the COVID-19 Pandemic. Anesthesiology. 2020;133（1）:16−18. DOI: 10.1097/aln.0000000000003341.
[23] Oreh A, Irechukwu C, Biyama F, et al. COVID-19 impact on Nigeria's national blood service commission - Lessons for Low- and Middle-Income Countries （LMICs）. Niger Postgrad Med J. 2022;29（1）:6−12. DOI: 10.4103/npmj.npmj_720_21.
[24] Maghsudlu M, Eshghi P, Amini Kafi-Abad S, et al. Blood supply sufficiency and safety management in Iran during the COVID-19 outbreak. Vox Sang. 2021;116（2）:175−180. DOI: 10.1111/vox.13012.
[25] Ong CW, Ho KY, Hsu LY, et al. Reacting to the emergence of swine-origin influenza A H1N1. Lancet Infect Dis. 2009;9（7）:397−398. DOI: 10.1016/s1473-3099（09）70159-8.
[26] Yahia AIO. Management of blood supply and demand during the COVID-19 pandemic in King Abdullah Hospital, Bisha, Saudi Arabia. Transfus Apher Sci. 2020;59（5）:102836. DOI: 10.1016/j.transci.2020.102836.
[27] Gniadek TJ, Mallek J, Wright G, et al. Expansion of hospital-based blood collections in the face of COVID-19 associated national blood shortage. Transfusion. 2020;60（7）:1470−1475. DOI: 10.1111/trf.15869.
[28] Schaefer R, Long T, Wampler D, et al. Operationalizing the Deployment of Low-Titer O-Positive Whole Blood Within a Regional Trauma System. Mil Med. 2021;186（Suppl 1）:391−399. DOI: 10.1093/milmed/usaa283.
[29] Tayfur I, Günaydin M, Suner S. Healthcare Service Access and Utilization among Syrian Refugees in Turkey. Ann Glob Health. 2019;85（1）. DOI: 10.5334/aogh.2353.
[30] Shinar E, Yahalom V, Silverman BG. Meeting blood requirements following terrorist attacks: the Israeli experience. Curr Opin Hematol. 2006;13（6）:452−456. DOI: 10.1097/01.moh.0000245691.32085.66.
[31] Soffer D, Klausner J, Bar-Zohar D, et al. Usage of blood products in multiple-casualty incidents: the experience of a level I trauma center in Israel. Arch Surg. 2008;143（10）:983−989. DOI: 10.1001/archsurg.143.10.983.
[32] Bala M, Kaufman T, Keidar A, et al. Defining the need for blood and blood products transfusion following suicide bombing attacks on a civilian population: A level I single-centre experience. Injury. 2014;45（1）:50−55. DOI: 10.1016/j.injury.2012.11.011.
[33] Schmidt PJ. Blood and disaster-supply and demand. N Engl J Med. 2002;346（8）:617−620. DOI: 10.1056/nejm200202213460813.
[34] Gammon RR, Rosenbaum L, Cooke R, et al. Maintaining adequate donations and a sustainable blood supply. Lessons learned. Transfusion. 2021;61（1）:294−302. DOI: 10.1111/trf.16145.
[35] Stanger SHW, Yates N, Wilding R, et al. Blood inventory management: hospital best practice. Transfus Med Rev. 2012;26（2）:153−163. DOI: 10.1016/j.tmrv.2011.09.001.
[36] Chang CJ, Li G, Zhang SQ, et al. Employing a Fuzzy-Based Grey Modeling Procedure to Forecast China's Sulfur Dioxide Emissions. Int J Environ Res Public Health. 2019;16（14）. DOI: 10.3390/ijerph16142504.
[37] Gittins M, Ashton C, Holden N, et al. Environmental Factors and Hyperacute Stroke Care Activity During the COVID-19 Pandemic: An Interrupted Time-Series Analysis. J Stroke Cerebrovasc Dis. 2020;29（11）:105229. DOI: 10.1016/j.jstrokecerebrovasdis.2020.105229.
[38] Li ZQ, Pan HQ, Liu Q, et al. Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China. Infect Dis Poverty. 2020;9（1）:151. DOI: 10.1186/s40249-020-00771-7.
[39] Stieb DM, Zheng C, Salama D, et al. Systematic review and meta-analysis of case-crossover and time-series studies of short term outdoor nitrogen dioxide exposure and ischemic heart disease morbidity. Environ Health. 2020;19（1）:47. DOI: 10.1186/s12940-020-00601-1.
[40] Fanoodi B, Malmir B, Jahantigh FF. Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models. Comput Biol Med. 2019;113:103415. DOI: 10.1016/j.compbiomed.2019.103415.
[41] Li Z, Li Y. A comparative study on the prediction of the BP artificial neural network model and the ARIMA model in the incidence of AIDS. BMC Med Inform Decis Mak. 2020;20（1）:1−13. DOI: 10.1186/s12911-020-01157-3.
[42] Guo K, Song S, Qiu L, et al. Prediction of Red Blood Cell Demand for Pediatric Patients Using a Time-Series Model: A Single-Center Study in China. Front Med （Lausanne）. 2022;9:706284. DOI: 10.3389/fmed.2022.706284.
[43] Guo Y, Feng Y, Qu F, et al. Prediction of hepatitis E using machine learning models. PLoS One. 2020;15（9）:e0237750. DOI: 10.1371/journal.pone.0237750.
[44] Li Q, Xu YG, Qi SX, et al. Application of an Autoregressive Integrated Moving Average Model for Predicting the Incidence of Hemorrhagic Fever with Renal Syndrome. Am J Trop Med Hyg. 2012;87（2）:364−370. DOI: 10.4269/ajtmh.2012.11-0472.
[45] Qian Y, Zhang X, Fei G, et al. Forecasting deaths of road traffic injuries in China using an artificial neural network. Traffic Inj Prev. 2020;21（6）:407−412. DOI: 10.1080/15389588.2020.1770238.
[46] Wang K, Song W, Li J, et al. The Use of an Autoregressive Integrated Moving Average Model for Prediction of the Incidence of Dysentery in Jiangsu, China. Asia Pac J Public Health. 2016;28（4）:336−346. DOI: 10.1177/1010539516645153.
[47] Earnest A, Tan SB, Wilder-Smith A, et al. Comparing statistical models to predict dengue fever notifications. Comput Math Methods Med. 2012;2012:758674. DOI: 10.1155/2012/758674.
[48] Sardar I, Akbar MA, Leiva V, et al. Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: methodology, evaluation, and case study in SAARC countries. Stoch Environ Res Risk Assess. 2023;37（1）:345−359. DOI: 10.1007/s00477-022-02307-x.
[49] Turkulainen EV, Wemelsfelder ML, Janssen MP, et al. A robust autonomous method for blood demand forecasting. Transfusion. 2022;62（6）:1261−1268. DOI: 10.1111/trf.16870.
[50] Fortsch SM, Khapalova EA. Reducing uncertainty in demand for blood. Oper Res Health Care. 2016;9:16−28. DOI: 10.1016/j.orhc.2016.02.002.
[51] Billah B, King ML, Snyder RD, et al. Exponential smoothing model selection for forecasting. Int J Forecast. 2006;22（2）:239−247. DOI: 10.1016/j.ijforecast.2005.08.002.
[52] Pereira A. Performance of time-series methods in forecasting the demand for red blood cell transfusion. Transfusion. 2004;44（5）:739−746. DOI: 10.1111/j.1537-2995.2004.03363.x.
[53] Cetin B, Yavuz I. Comparison of forecast accuracy of Ata and exponential smoothing. J Appl Stat. 2021;48（13-15）:2580−2590. DOI: 10.1080/02664763.2020.1803813.
[54] Piłatowska M. Combined Forecasts Using the Akaike Weights. Dynamic Econometric Models. 2009;9（0）:5−16. DOI: 10.12775/dem.2009.001.
[55] Kourentzes N, Petropoulos F, Trapero JR. Improving forecasting by estimating time series structural components across multiple frequencies. Int J Forecast. 2014;30（2）:291−302. DOI: 10.1016/j.ijforecast.2013.09.006.
[56] Francis DJ. An introduction to structural equation models. J Clin Exp Neuropsychol. 1988;10（5）:623−639. DOI: 10.1080/01688638808402800.
[57] Torrent-Sellens J, Salazar-Concha C, Ficapal-Cusí P, et al. Using Digital Platforms to Promote Blood Donation: Motivational and Preliminary Evidence from Latin America and Spain. Int J Environ Res Public Health. 2021;18（8）:4270. DOI: 10.3390/ijerph18084270.
[58] Griffin D, Grace D, O'Cass A. Blood Donation: Comparing Individual Characteristics, Attitudes, and Feelings of Donors and Nondonors. Health Mark Q. 2014;31（3）:197−212. DOI: 10.1080/07359683.2014.936276.
[59] Yoshida K, Shimizu Y, Yoshimoto J, et al. Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression. PLoS One. 2017;12（7）:e0179638. DOI: 10.1371/journal.pone.0179638.
[60] Saunders CH, Petersen CL, Durand MA, et al. Bring on the Machines: Could Machine Learning Improve the Quality of Patient Education Materials? A Systematic Search and Rapid Review. JCO Clin Cancer Inform. 2018;2:1−16. DOI: 10.1200/cci.18.00010.
[61] Yin P, Mao N, Chen H, et al. Machine and Deep Learning Based Radiomics Models for Preoperative Prediction of Benign and Malignant Sacral Tumors. Front Oncol. 2020;10:564725. DOI: 10.3389/fonc.2020.564725.
[62] Chan YK, Chen YF, Pham T, et al. Artificial Intelligence in Medical Applications. J Healthc Eng. 2018;2018:4827875. DOI: 10.1155/2018/4827875.
[63] Torang A, Gupta P, Klinke DJ 2nd. An elastic-net logistic regression approach to generate classifiers and gene signatures for types of immune cells and T helper cell subsets. BMC Bioinformatics. 2019;20（1）:433. DOI: 10.1186/s12859-019-2994-z.
[64] Nabet C, Chaline A, Franetich JF, et al. Prediction of malaria transmission drivers in Anopheles mosquitoes using artificial intelligence coupled to MALDI-TOF mass spectrometry. Sci Rep. 2020;10（1）:11379. DOI: 10.1038/s41598-020-68272-z.
[65] Razavi-Termeh SV, Sadeghi-Niaraki A, Choi SM. Asthma-prone areas modeling using a machine learning model. Sci Rep. 2021;11（1）:1912. DOI: 10.1038/s41598-021-81147-1.
[66] Sun J, Zhong G, Huang K, et al. Banzhaf random forests: Cooperative game theory based random forests with consistency. Neural Netw. 2018;106:20−29. DOI: 10.1016/j.neunet.2018.06.006.
[67] Huang X, Wang Y, Chen B, et al. Ability of a Machine Learning Algorithm to Predict the Need for Perioperative Red Blood Cells Transfusion in Pelvic Fracture Patients: A Multicenter Cohort Study in China. Front Med （Lausanne）. 2021;8. DOI: 10.3389/fmed.2021.694733.
[68] Hou N, Li M, He L, et al. Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost. J Transl Med. 2020;18（1）:462. DOI: 10.1186/s12967-020-02620-5.
[69] Hamed EA-R, Salem MA-M, Badr NL, et al. An Efficient Combination of Convolutional Neural Network and LightGBM Algorithm for Lung Cancer Histopathology Classification. Diagnostics （Basel）. 2023;13（15）:2469. DOI: 10.3390/diagnostics13152469.
[70] Zhang C, Lei X, Liu L. Predicting Metabolite-Disease Associations Based on LightGBM Model. Front Genet. 2021;12. DOI: 10.3389/fgene.2021.660275.
[71] Radakovich N, Sallman DA, Buckstein R, et al. A machine learning model of response to hypomethylating agents in myelodysplastic syndromes. iScience. 2022;25:104931. DOI: 10.1016/j.isci.2022.104931.
[72] Inoue T, Ichikawa D, Ueno T, et al. XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury. Neurotrauma Rep. 2020;1（1）:8−16. DOI: 10.1089/neur.2020.0009.
[73] Sun X, Xu Z, Feng Y, et al. RBC Inventory-Management System Based on XGBoost Model. Indian J Hematol Blood Transfus. 2021;37（1）:126−133. DOI: 10.1007/s12288-020-01333-5.
[74] Kawakita S, Beaumont JL, Jucaud V, et al. Personalized prediction of delayed graft function for recipients of deceased donor kidney transplants with machine learning. Sci Rep. 2020;10:18409. DOI: 10.1038/s41598-020-75473-z.
[75] Strickland M, Nguyen A, Wu S, et al. Assessment of Machine Learning Methods to Predict Massive Blood Transfusion in Trauma. World J Surg. 2023;47（10）:2340−2346. DOI: 10.1007/s00268-023-07098-y.
[76] Afzal MAF, Sonpal A, Haghighatlari M, et al. A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules. Chem Sci. 2019;10（36）:8374−8383. DOI: 10.1039/c9sc02677k.
[77] Koo KC, Lee KS, Kim S, et al. Long short-term memory artificial neural network model for prediction of prostate cancer survival outcomes according to initial treatment strategy: development of an online decision-making support system. World J Urol. 2020;38（10）:2469−2476. DOI: 10.1007/s00345-020-03080-8.
[78] Lim HH, Cheon E, Lee DH, et al. Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging. Sensors （Basel）. 2020;20（6）:1611. DOI: 10.3390/s20061611.
[79] Jia Z, Zhou S, Su Q, et al. Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal（loid）s Pollution Based on Kriging Interpolation and BP Neural Network. Int J Environ Res Public Health. 2017;15（1）:34. DOI: 10.3390/ijerph15010034.
[80] Norouzi N, Zarazua de Rubens G, Choupanpiesheh S, et al. When pandemics impact economies and climate change: Exploring the impacts of COVID-19 on oil and electricity demand in China. Energy Res Soc Sci. 2020;68:101654. DOI: 10.1016/j.erss.2020.101654.
[81] Tang S, Davarmanesh P, Song Y, et al. Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data. J Am Med Inform Assoc. 2020;27（12）:1921−1934. DOI: 10.1093/jamia/ocaa139.
[82] Couronné R, Probst P, Boulesteix AL. Random forest versus logistic regression: a large-scale benchmark experiment. BMC Bioinformatics. 2018;19:270. DOI: 10.1186/s12859-018-2264-5.
[83] Alakus TB, Turkoglu I. Comparison of deep learning approaches to predict COVID-19 infection. Chaos Solitons Fractals. 2020;140. DOI: 10.1016/j.chaos.2020.110120.
[84] Durand WM, DePasse JM, Daniels AH. Predictive Modeling for Blood Transfusion After Adult Spinal Deformity Surgery. Spine. 2018;43（15）:1058−1066. DOI: 10.1097/brs.0000000000002515.
[85] Lee BH, Lee KS, Kim HI, et al. Blood Transfusion, All-Cause Mortality and Hospitalization Period in COVID-19 Patients: Machine Learning Analysis of National Health Insurance Claims Data. Diagnostics （Basel）. 2022;12（12）:2970. DOI: 10.3390/diagnostics12122970.
[86] AlJame M, Imtiaz A, Ahmad I, et al. Deep forest model for diagnosing COVID-19 from routine blood tests. Sci Rep. 2021;11:16682. DOI: 10.1038/s41598-021-95957-w.
[87] Aziz RM, Baluch MF, Patel S, et al. LGBM: a machine learning approach for Ethereum fraud detection. Int J Inf Technol. 2022;14（7）:3321−3331. DOI: 10.1007/s41870-022-00864-6.
[88] Mishra S, Daga A, Gupta A. Inventory management practices in the blood bank of an institute of national importance in India. J Family Med Prim Care. 2021;10（12）:4489−4492. DOI: 10.4103/jfmpc.jfmpc_1000_21.
[89] Kotwal RS, Howard JT, Orman JA, et al. The Effect of a Golden Hour Policy on the Morbidity and Mortality of Combat Casualties. JAMA Surg. 2016;151（1）:15−24. DOI: 10.1001/jamasurg.2015.3104.
[90] Leal-Noval SR, Rincon-Ferrari MD, Munoz-Gomez M. Red blood cell transfusion may be more detrimental than anemia for the clinical outcome of patients with severe traumatic brain injury. Crit Care. 2019;23（1）:189. DOI: 10.1186/s13054-019-2470-1.
[91] Ge Y, Rosendahl P, Duran C, et al. Cell Mechanics Based Computational Classification of Red Blood Cells Via Machine Intelligence Applied to Morpho-Rheological Markers. IEEE/ACM Trans Comput Biol Bioinform. 2021;18（4）:1405−1415. DOI: 10.1109/tcbb.2019.2945762.
[92] Hussein E, Teruya J. Evaluation of blood supply operation and infectious disease markers in blood donors during the Egyptian revolution. Transfusion. 2012;52（11）:2321−2328. DOI: 10.1111/j.1537-2995.2012.03592.x.
[93] Liu J, Huang Y, Wang J, et al. Impact of the May 12, 2008, earthquake on blood donations across five Chinese blood centers. Transfusion. 2010;50（9）:1972−1979. DOI: 10.1111/j.1537-2995.2010.02665.x.
[94] Irving AH, Harris A, Petrie D, et al. A systematic review and network meta-analysis of incentive- and non-incentive-based interventions for increasing blood donations. Vox Sang. 2020;115（4）:275−287. DOI: 10.1111/vox.12881.
[95] Sass RG. Toward a more stable blood supply: charitable incentives, donation rates, and the experience of September 11. Am J Bioeth. 2013;13（6）:38−45. DOI: 10.1080/15265161.2013.781703.
[96] Shortt J, Polizzotto MN, Waters N, et al. Assessment of the urgency and deferability of transfusion to inform emergency blood planning and triage: the Bloodhound prospective audit of red blood cell use. Transfusion. 2009;49（11）:2296−2303. DOI: 10.1111/j.1537-2995.2009.02305.x.
[97] Hoeven LRV, Bruijne MC, Kemper PF, et al. Validation of multisource electronic health record data: an application to blood transfusion data. BMC Med Inform Decis Mak. 2017;17（1）:107. DOI: 10.1186/s12911-017-0504-7.
[98] Lin SW, Anisa KN, Chen YC. Effects of meteorological factors on blood donation. Transfus Apher Sci. 2020;59:102901. DOI: 10.1016/j.transci.2020.102901.
[99] Samani MRG, Hosseini-Motlagh SM. A robust framework for designing blood network in disaster relief: a real-life case. Oper Res. 2021;21（3）:1529−1568. DOI: 10.1007/s12351-020-00588-0.
[100] Farrokhizadeh E, Seyfi-Shishavan SA, Satoglu SI. Blood supply planning during natural disasters under uncertainty: a novel bi-objective model and an application for red crescent. Ann Oper Res. 2022;319（1）:73−113. DOI: 10.1007/s10479-021-03978-5.
[101] Liu S, Zhou R, Xia XQ, et al. Machine learning models to predict red blood cell transfusion in patients undergoing mitral valve surgery. Ann Transl Med. 2021;9（7）:530. DOI: 10.21037/atm-20-7375.
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