• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies;Workshop on computational linguistics and clinical psychology: from linguistic signal to clinical reality >Detecting late-life depression in Alzheimer's disease through analysis of speech and language
【24h】

Detecting late-life depression in Alzheimer's disease through analysis of speech and language

机译：通过语音和语言分析检测阿尔茨海默氏病的晚期抑郁症

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

摘要

Alzheimer's disease (AD) and depression share a number of symptoms, and commonly occur together. Being able to differentiate between these two conditions is critical, as depression is generally treatable. We use linguistic analysis and machine learning to determine whether automated screening algorithms for AD are affected by depression, and to detect when individuals diagnosed with AD are also showing signs of depression. In the first case, we find that our automated AD screening procedure does not show false positives for individuals who have depression but are otherwise healthy. In the second case, we have moderate success in detecting signs of depression in AD (accuracy = 0.658), but we are not able to draw a strong conclusion about the features that are most informative to the classification.
机译：阿尔茨海默氏病（AD）和抑郁症有许多症状，并且通常同时发生。能够区分这两种情况至关重要，因为通常可以治疗抑郁症。我们使用语言分析和机器学习来确定AD的自动筛选算法是否受抑郁症影响，并检测被诊断患有AD的个体何时也表现出抑郁症的迹象。在第一种情况下，我们发现针对患有抑郁症但其他方面健康的个体，我们的自动AD筛查程序不会显示出假阳性。在第二种情况下，我们在检测AD抑郁症状方面有一定的成功（准确度= 0.658），但是对于最能帮助分类的特征我们无法得出有力的结论。

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