Prediction of cervical spine injury in young pediatric patients: an optimal trees artificial intelligence approach

Bertsimas Dimitris; Masiakos Peter T.; Mylonas Konstantinos S.; Wiberg Holly

首页> 外文期刊>Journal of Pediatric Surgery: Official Journal of the Surgical Section of the American Academy of Pediatric, the British Association of Paediatric Surgeons, the American Pediatric Surgical Association, and the Canadian Association of Paediatric Surgeons >Prediction of cervical spine injury in young pediatric patients: an optimal trees artificial intelligence approach

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Prediction of cervical spine injury in young pediatric patients: an optimal trees artificial intelligence approach

机译：幼儿患者颈椎损伤预测：最优树木人工智能方法

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Background: Cervical spine injuries (CSI) are a major concern in young pediatric trauma patients. The consequences of missed injuries and difficulties in injury clearance for non-verbal patients have led to a tendency to image young children. Imaging, particularly computed tomography (CT) scans, presents risks including radiation-induced carcinogenesis. In this study we leverage machine learning methods to develop highly accurate clinical decision rules to predict pediatric CSI.

机译：背景：颈椎损伤（CSI）是年轻儿科创伤患者的主要问题。错过伤害的后果和非言语患者伤害清关的困难导致了对象幼儿的趋势。成像，特别是计算机断层扫描（CT）扫描，呈现出包括辐射诱导的致癌的风险。在这项研究中，我们利用机器学习方法来开发高度准确的临床决策规则，以预测儿科CSI。

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《Journal of Pediatric Surgery: Official Journal of the Surgical Section of the American Academy of Pediatric, the British Association of Paediatric Surgeons, the American Pediatric Surgical Association, and the Canadian Association of Paediatric Surgeons》 |2019年第11期|共5页
作者
Bertsimas Dimitris; Masiakos Peter T.; Mylonas Konstantinos S.; Wiberg Holly;
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作者单位

MIT Operat Res Ctr 77 Massachusetts Ave Cambridge MA 02139 USA;

Harvard Med Sch Massachusetts Gen Hosp Div Pediat Surg Warren 11 55 Fruit St Boston MA 02114;

Harvard Med Sch Massachusetts Gen Hosp Div Pediat Surg Warren 11 55 Fruit St Boston MA 02114;

MIT Operat Res Ctr 77 Massachusetts Ave Cambridge MA 02139 USA;

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原文格式 PDF
正文语种 eng
中图分类儿科学;
关键词
Cervical spine trauma; Cervical spine injury; Artificial intelligence; Machine learning; Optimal classification trees;

机译：颈椎创伤;颈椎损伤;人工智能;机器学习;最优分类树木;

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1. Prediction of cervical spine injury in young pediatric patients: an optimal trees artificial intelligence approach [J] . Bertsimas Dimitris, Masiakos Peter T., Mylonas Konstantinos S., Journal of Pediatric Surgery: Official Journal of the Surgical Section of the American Academy of Pediatric, the British Association of Paediatric Surgeons, the American Pediatric Surgical Association, and the Canadian Association of Paediatric Surgeons . 2019,第11期

机译：幼儿患者颈椎损伤预测：最优树木人工智能方法
2. Validity and reliability of clinical prediction rules used to screen for cervical spine injury in alert low-risk patients with blunt trauma to the neck: part 2. A systematic review from the Cervical Assessment and Diagnosis Research Evaluation (CADRE) Collaboration [J] . Moser N., Lemeunier N., Southerst D., European spine journal: official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society . 2018,第6期

机译：临床预测规则的有效性和可靠性，用于颈椎损伤的癌症患者对颈部钝性创伤的警报低风险患者：第2部分系统审查宫颈评估和诊断研究评估（CADRE）协作
3. Screening cervical spine MRI after normal cervical spine CT scans in patients in whom cervical spine injury cannot be excluded by physical examination. [J] . Steigelman M, Lopez P, Dent D, The American Journal of Surgery . 2008,第6期

机译：不能通过体格检查排除颈椎损伤的患者，在正常颈椎CT扫描后筛查颈椎MRI。
4. Inertially-lnduced Cervical Spine Injuries in the Pediatric Population [C] . Tara Moore, Michael Prange, Catherine Corrigan Biomechanics, 2009 . 2009

机译：儿科人群惯性诱发的颈椎损伤
5. Development of a Clinical Prediction Rule to Identify Patients with Neck Pain Likely to Benefit from Cervical Spine Manipulation and a Range of Motion Exercise. [D] . Puentedura, Emilio Joseph. 2011

机译：制定临床预测规则以识别颈痛患者可能从颈椎操纵和一系列运动锻炼中受益。
6. Using Artificial Intelligence to Obtain More Evidence? Prediction of Length of Hospitalization in Pediatric Burn Patients [O] . Julia Elrod, Christoph Mohr, Ruben Wolff, 2020

机译：使用人工智能获得更多证据？儿科烧伤患者住院时间长度预测
7. Posterior Cervical Fixation with Nitinol Shape Memory Loop in the Anterior-Posterior Combined Approach for the Patients with Three Column Injury of the Cervical Spine : Preliminary Report [O] . Yu, Dong-Kun, Heo, Dong-Hwa, Cho, Sung-Min, 2008

机译：颈椎三柱损伤患者前后路联合镍钛合金形状记忆环后路颈椎固定术

Prediction of cervical spine injury in young pediatric patients: an optimal trees artificial intelligence approach

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