Machine Learning Applied to Registry Data: Development of a Patient-Specific Prediction Model for Blood Transfusion Requirements During Craniofacial Surgery Using the Pediatric Craniofacial Perioperative Registry Dataset

Jalali Ali; Lonsdale Hannah; Zamora Lillian V; Ahumada Luis; Nguyen Anh Thy H.; Rehman Mohamed; Fackler James; Stricker Paul A.; Fernandez Allison M.

首页> 外文期刊>Anesthesia and Analgesia: Journal of the International Anesthesia Research Society >Machine Learning Applied to Registry Data: Development of a Patient-Specific Prediction Model for Blood Transfusion Requirements During Craniofacial Surgery Using the Pediatric Craniofacial Perioperative Registry Dataset

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Machine Learning Applied to Registry Data: Development of a Patient-Specific Prediction Model for Blood Transfusion Requirements During Craniofacial Surgery Using the Pediatric Craniofacial Perioperative Registry Dataset

机译：机器学习应用于注册表数据：使用小儿颅面外围手术期登记区数据集进行颅面手术期间出血预测模型的患者特异性预测模型的开发

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《Anesthesia and Analgesia: Journal of the International Anesthesia Research Society》 |2021年第1期|共12页
作者
Jalali Ali; Lonsdale Hannah; Zamora Lillian V; Ahumada Luis; Nguyen Anh Thy H.; Rehman Mohamed; Fackler James; Stricker Paul A.; Fernandez Allison M.;
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作者单位

Johns Hopkins All Childrens Hosp St Petersburg FL 33701 USA;

Johns Hopkins All Childrens Hosp Dept Anesthesia Perioperat &

Pain Med 501 6th Ave S St;

Johns Hopkins All Childrens Hosp Dept Anesthesia Perioperat &

Pain Med 501 6th Ave S St;

Johns Hopkins All Childrens Hosp St Petersburg FL 33701 USA;

Johns Hopkins All Childrens Hosp Predict Analyt Core St Petersburg FL 33701 USA;

Johns Hopkins All Childrens Hosp Dept Anesthesia Perioperat &

Pain Med 501 6th Ave S St;

Johns Hopkins Univ Sch Med Dept Anesthesiol &

Crit Care Med Baltimore MD 21205 USA;

Univ Penn Childrens Hosp Philadelphia Dept Anesthesiol &

Crit Care Med Perelman Sch Med;

Johns Hopkins All Childrens Hosp Dept Anesthesia Perioperat &

Pain Med 501 6th Ave S St;

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正文语种 eng
中图分类麻醉学;
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2. Shear stress distribution prediction in symmetric compound channels using data mining and machine learning models [J] . Zohreh SHEIKH KHOZANI, Khabat KHOSRAVI, Mohammadamin TORABI, 结构与土木工程前沿：英文版 . 2020,第005期
3. Aminocaproic acid administration is associated with reduced perioperative blood loss and transfusion in pediatric craniofacial surgery [J] . Hsu G., Taylor J. A., Fiadjoe J. E., Acta Anaesthesiologica Scandinavica . 2016,第2期

机译：氨基己酸的使用与小儿颅面外科手术围手术期失血和输血减少有关
4. Aprotinin and transfusion requirements in pediatric craniofacial surgery [J] . AhmedZ., StrickerL., RozzelleA., Paediatric anaesthesia . 2014,第2期

机译：小儿颅面外科手术中抑肽酶和输血的要求
5. Intra- and Postoperative Blood Loss and Transfusion Requirements in Children Undergoing Craniofacial Surgery [J] . DAmore Alexander Lindholm, Rasmussen Mads, Christensen Leif, The Journal of craniofacial surgery . 2019,第6期

机译：颅外手术中儿童的内部和术后失血和输血要求
6. Predicting Unemployment with Machine Learning Based on Registry Data [C] . Markus Viljanen, Tapio Pahikkala International Conference on Research Challenges in Information Science . 2020

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7. Multidisciplinary Applications of U.S. Soil Datasets: Machine Learning Models, Data Mining, and Land Use Analyses. [D] . Amanda M. Ramcharan. 2017

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8. Improvement in Mortality Risk Prediction Following Percutaneous Coronary Intervention Through Addition of a Compassionate Use Variable to the National Cardiovascular Data Registry CathPCI® Dataset: A Study from the Massachusetts Angioplasty Registry [O] . Frederic S. Resnic, Sharon-Lise T. Normand, Thomas C. Piemonte, -1

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9. Improvement in Mortality Risk Prediction After Percutaneous Coronary Intervention Through the Addition of a “Compassionate Use” Variable to the National Cardiovascular Data Registry CathPCI Dataset A Study From the Massachusetts Angioplasty Registry [O] . Resnic Frederic S., Normand Sharon-Lise T., Piemonte Thomas C., 2011

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10. Development of a Model for the Identification of High Risk Occupational Groups Using RTECS (Registry of Toxic Effects of Chemical Substances) and NOHS (National Occupational Hazard Survey) Data [R] . Pielmeier, G. R. 1981

机译：利用RTECs（化学物质毒性登记处）和NOHs（国家职业危害调查）数据建立高风险职业群体识别模型

Machine Learning Applied to Registry Data: Development of a Patient-Specific Prediction Model for Blood Transfusion Requirements During Craniofacial Surgery Using the Pediatric Craniofacial Perioperative Registry Dataset

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