• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE transactions on information technology in biomedicine >A support vectors classifier approach to predicting the risk of progression of adolescent idiopathic scoliosis
【24h】

A support vectors classifier approach to predicting the risk of progression of adolescent idiopathic scoliosis

机译：一种支持向量分类器方法，用于预测青少年特发性脊柱侧弯进展的风险

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

A support vector classifier (SVC) approach was employed in predicting the risk of progression of adolescent idiopathic scoliosis (AIS), a condition that causes visible trunk asymmetries. As the aetiology of AIS is unknown, its risk of progression can only be predicted from measured indicators. Previous studies suggest that individual indicators of AIS do not reliably predict its risk of progression. Complex indicators with better predictive values have been developed but are unsuitable for clinical use as obtaining their values is often onerous, involving much skill and repeated measurements taken over time. Based on the hypothesis that combining common indicators of AIS using an SVC approach would produce better prediction results more quickly, we conducted a study using three datasets comprising a total of 44 moderate AIS patients (30 observed, 14 treated with brace). Of the 44 patients, 13 progressed less than 5/spl deg/ and 31 progressed more than 5/spl deg/. One dataset comprised all the patients. A second dataset comprised all the observed patients and a third comprised all the brace-treated patients. Twenty-one radiographic and clinical indicators were obtained for each patient. The result of testing on the three datasets showed that the system achieved 100% accuracy in training and 65%-80% accuracy in testing. It outperformed a "statistically equivalent" logistic regression model and a stepwise linear regression model on the said datasets. It took less than 20 min per patient to measure the indicators, input their values into the system, and produce the needed results, making the system viable for use in a clinical environment.
机译：支持向量分类器（SVC）方法用于预测青春期特发性脊柱侧弯（AIS）的发展风险，这种疾病会导致躯干不对称。由于AIS的病因尚不清楚，因此其进展风险只能根据已测指标进行预测。先前的研究表明，AIS的单独指标不能可靠地预测其进展风险。已经开发出具有更好预测值的复杂指标，但不适合临床使用，因为获取它们的值通常很麻烦，涉及许多技能，并且随着时间的推移会重复进行测量。基于这样的假设，即使用SVC方法将AIS的共同指标结合起来可以更快地产生更好的预测结果，我们使用了三个数据集进行了一项研究，该数据集总共包括44位中度AIS患者（观察到30位患者，其中14位用支架治疗）。在这44名患者中，有13名进展少于5 / spl deg /，而31名进展超过5 / spl deg /。一个数据集包括所有患者。第二个数据集包含所有观察到的患者，第三个数据集包含所有矫正治疗的患者。为每位患者获得二十一项放射学和临床指标。对这三个数据集进行测试的结果表明，该系统在训练中达到了100％的准确性，在测试中达到了65％-80％的准确性。它在所述数据集上的表现优于“统计等效”的逻辑回归模型和逐步线性回归模型。每位患者花费不到20分钟的时间来测量指标，将指标值输入系统并产生所需的结果，从而使该系统可在临床环境中使用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号