Comparison of Machine Learning Optimal Classification Trees With the Pediatric Emergency Care Applied Research Network Head Trauma Decision Rules

Dimitris Bertsimas; Jack Dunn; Dale W. Steele; Thomas A. Trikalinos; Yuchen Wang

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Comparison of Machine Learning Optimal Classification Trees With the Pediatric Emergency Care Applied Research Network Head Trauma Decision Rules

机译：机器学习最佳分类树与儿科紧急护理应用研究网络头创伤决策规则

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IMPORTANCE Computed tomographic (CT) scanning is the standard for the rapid diagnosis of intracranial injury, but it is costly and exposes patients to ionizing radiation. The Pediatric Emergency Care Applied Research Network (PECARN) rules for identifying children with minor head trauma who are at very low risk of clinically important traumatic brain injury (ciTBI) are widely used to triage CT imaging. OBJECTIVE To examine whether optimal classification trees (OCTs), which are novel machine-learning classifiers, improve on PECARN rules' predictive accuracy.

机译：重要性计算断层扫描（CT）扫描是颅内损伤快速诊断的标准，但它昂贵并使患者暴露于电离辐射。鉴定临床上重要创伤性脑损伤（CITBI）风险非常低的小头创伤患儿的儿科紧急护理应用研究网络（PECARN）规则被广泛用于分类CT成像。目的探讨最佳分类树（OCT）是新颖的机器学习分类器，提高PECARN规则的预测准确性。

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《JAMA pediatrics》 |2019年第7期|共9页
作者
Dimitris Bertsimas; Jack Dunn; Dale W. Steele; Thomas A. Trikalinos; Yuchen Wang;
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Operations Research Center Massachusetts Institute of Technology Cambridge;

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正文语种 eng
中图分类儿科学;
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1. Comparison of Machine Learning Optimal Classification Trees With the Pediatric Emergency Care Applied Research Network Head Trauma Decision Rules [J] . Dimitris Bertsimas, Jack Dunn, Dale W. Steele, JAMA pediatrics . 2019,第7期

机译：机器学习最佳分类树与儿科紧急护理应用研究网络头创伤决策规则
2. Clinical Judgment and the Pediatric Emergency Care Applied Research Network Head Trauma Prediction Rules [J] . David R. Vinson, Dustin W. Ballard, Peter S. Dayan, Annals of Emergency Medicine: Journal of the American College of Emergency Physicians and the University Association for Emergency Medicine . 2018,第3期

机译：临床判断与儿科急诊护理应用研究网络头创伤预测规则
3. Impact of the Pediatric Emergency Care Applied Research Network (PECARN) guidelines on emergency department use of head computed tomography at a level I safety-net trauma center [J] . Noam Gerber, Kelley Sookraj, Swapna Munnangi, Emergency radiology . 2019,第1期

机译：儿科紧急护理应用研究网络（PECARN）对急诊部门使用头部计算断层扫描的影响在I级安全网创伤中心
4. Challenges of Disposition Decision Making for Pediatric Trauma Patients in the Emergency Department [C] . Bat-Zion Hose, Pascale Carayon, Peter Hoonakker, International Ergonomics Association, Congress . 2018

机译：急诊部儿科创伤患者处置决策挑战
5. Application of Engineering Principles with a Comparison of Machine Learning Classification Methods to Predict Treatment Outcomes in Head and Neck Cancer Patients. [D] . Hernandez, Alberto Miranda. 2016

机译：工程原理与机器学习分类方法的比较在预测头颈癌患者治疗结果中的应用。
6. 2107. Decision Trees vs. Neural Networks for Supervised Machine Learning-Based Prediction of Healthcare-Associated Urinary Tract Infections [O] . Philip Zachariah, Elioth Mirsha Sanabria Buenaventura, Jianfang Liu, -1

机译：2107.基于决策树与神经网络的基于监督的机器学习对医疗保健相关的尿路感染的预测
7. Comparison of Machine Learning Optimal Classification Trees With the Pediatric Emergency Care Applied Research Network Head Trauma Decision Rules [O] . Dimitris Bertsimas, Jack Dunn, Dale W. Steele, 2019

机译：机器学习最佳分类树与儿科紧急护理应用研究网络头创伤决策规则

Comparison of Machine Learning Optimal Classification Trees With the Pediatric Emergency Care Applied Research Network Head Trauma Decision Rules

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