• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IAAI-12;Innovative applications of artificial intelligence conference;AAAI conference on artificial intelligence;Symposium on educational advances in artificial intelligence;AAAI-12;EAAI-12 >Cost-Sensitive Risk Stratification in the Diagnosis of Heart Disease
【24h】

Cost-Sensitive Risk Stratification in the Diagnosis of Heart Disease

机译：心脏病诊断中的成本敏感型风险分层

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We investigate machine learning methods for diagnostic screening of heart disease. Coronary heart disease is the leading cause of death in the US, causing more deaths than all types of cancers combined. Early diagnosis of heart disease in women is harder than it is in men and typically requires the administration of several clinical tests on the patient. Most risk stratification methods aggregate the results of such tests, including the risky, invasive procedures that cannot be administered on all patients. In this paper, our goal is to identify patients who are under high-risk of having heart disease and related adverse events, using a minimal number of diagnostic tests, especially less invasive ones. The low frequency of patients with severe heart disease in the dataset is challenging for most conventional machine learning methods. To overcome this problem, we develop and apply a cost-sensitive k nearest neighbor algorithm. Our contributions are two fold: First, we compare the predictive value of several diagnostic procedures for heart disease, including electrocardiography, angiography, radionuclide perfusion and conclude that in womens heart disease, certain combinations of non-invasive techniques are more predictive than some of the widely used invasive procedures. Then, we evaluate held out data and achieve an AUROC over 0.70, signifying valuable clinical utility, using only the least costly and least invasive tests.
机译：我们研究了用于心脏病诊断筛查的机器学习方法。在美国，冠心病是最主要的死亡原因，其死亡人数超过所有类型癌症的总和。女性心脏病的早期诊断要比男性困难，并且通常需要对患者进行多项临床检查。大多数风险分层方法汇总了此类测试的结果，包括无法对所有患者进行的高风险，侵入性程序。在本文中，我们的目标是使用最少数量的诊断测试（尤其是侵入性较小的测试）来识别患有心脏病和相关不良事件的高风险患者。对于大多数传统的机器学习方法，数据集中患有严重心脏病的患者的低频率具有挑战性。为了克服这个问题，我们开发并应用了一种成本敏感的k最近邻算法。我们的贡献有两个方面：首先，我们比较了几种心脏病诊断方法的预测价值，包括心电图，血管造影，放射性核素灌注，并得出结论，在女性心脏病中，非侵入性技术的某些组合比某些诊断方法更具预测性。广泛使用的侵入性程序。然后，我们评估保留的数据并获得AUROC超过0.70，这意味着仅使用成本最低且侵入性最小的测试即可得到有价值的临床实用性。

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