从语义语言流畅性表现预测痴呆筛查和分期得分

N. Linz, J. Tröger, Jan Alexandersson, Maria Wolters, A. König, Philippe H. Robert
{"title":"从语义语言流畅性表现预测痴呆筛查和分期得分","authors":"N. Linz, J. Tröger, Jan Alexandersson, Maria Wolters, A. König, Philippe H. Robert","doi":"10.1109/ICDMW.2017.100","DOIUrl":null,"url":null,"abstract":"The standard dementia screening tool Mini Mental State Examination (MMSE) and the standard dementia staging tool Clinical Dementia Rating Scale (CDR) are prominent methods for answering questions whether a person might have dementia and about the dementia severity respectively. These methods are time consuming and require well-educated personnel to administer. Conversely, cognitive tests, such as the Semantic Verbal Fluency (SVF), demand little time. With this as a starting point, we investigate the relation between SVF results and MMSE/CDR-SOB scores. We use regression models to predict scores based on persons' SVF performance. Over a set of 179 patients with different degree of dementia, we achieve a mean absolute error of of 2.2 for MMSE (range 0–30) and 1.7 for CDR-SOB (range 0–18). True and predicted scores agree with a Cohen's κ of 0.76 for MMSE and 0.52 for CDR-SOB. We conclude that our approach has potential to serve as a cheap dementia screening, possibly even in non-clinical settings.","PeriodicalId":389183,"journal":{"name":"2017 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":"{\"title\":\"Predicting Dementia Screening and Staging Scores from Semantic Verbal Fluency Performance\",\"authors\":\"N. Linz, J. Tröger, Jan Alexandersson, Maria Wolters, A. König, Philippe H. Robert\",\"doi\":\"10.1109/ICDMW.2017.100\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The standard dementia screening tool Mini Mental State Examination (MMSE) and the standard dementia staging tool Clinical Dementia Rating Scale (CDR) are prominent methods for answering questions whether a person might have dementia and about the dementia severity respectively. These methods are time consuming and require well-educated personnel to administer. Conversely, cognitive tests, such as the Semantic Verbal Fluency (SVF), demand little time. With this as a starting point, we investigate the relation between SVF results and MMSE/CDR-SOB scores. We use regression models to predict scores based on persons' SVF performance. Over a set of 179 patients with different degree of dementia, we achieve a mean absolute error of of 2.2 for MMSE (range 0–30) and 1.7 for CDR-SOB (range 0–18). True and predicted scores agree with a Cohen's κ of 0.76 for MMSE and 0.52 for CDR-SOB. We conclude that our approach has potential to serve as a cheap dementia screening, possibly even in non-clinical settings.\",\"PeriodicalId\":389183,\"journal\":{\"name\":\"2017 IEEE International Conference on Data Mining Workshops (ICDMW)\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE International Conference on Data Mining Workshops (ICDMW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDMW.2017.100\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Data Mining Workshops (ICDMW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMW.2017.100","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 24

摘要

标准的痴呆症筛查工具迷你精神状态检查(MMSE)和标准的痴呆症分期工具临床痴呆症评定量表(CDR)分别是回答一个人是否可能患有痴呆症和痴呆症严重程度的重要方法。这些方法耗时长,需要受过良好教育的人员来管理。相反,认知测试,如语义语言流畅性(SVF),只需要很少的时间。以此为出发点,我们研究了SVF结果与MMSE/CDR-SOB分数之间的关系。我们使用回归模型来预测基于人的SVF表现的分数。在179名不同程度痴呆患者中,MMSE的平均绝对误差为2.2(范围0-30),CDR-SOB的平均绝对误差为1.7(范围0-18)。MMSE的真实和预测分数符合科恩κ 0.76和CDR-SOB的0.52。我们的结论是,我们的方法有潜力作为一种廉价的痴呆症筛查,甚至可能在非临床环境中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting Dementia Screening and Staging Scores from Semantic Verbal Fluency Performance
The standard dementia screening tool Mini Mental State Examination (MMSE) and the standard dementia staging tool Clinical Dementia Rating Scale (CDR) are prominent methods for answering questions whether a person might have dementia and about the dementia severity respectively. These methods are time consuming and require well-educated personnel to administer. Conversely, cognitive tests, such as the Semantic Verbal Fluency (SVF), demand little time. With this as a starting point, we investigate the relation between SVF results and MMSE/CDR-SOB scores. We use regression models to predict scores based on persons' SVF performance. Over a set of 179 patients with different degree of dementia, we achieve a mean absolute error of of 2.2 for MMSE (range 0–30) and 1.7 for CDR-SOB (range 0–18). True and predicted scores agree with a Cohen's κ of 0.76 for MMSE and 0.52 for CDR-SOB. We conclude that our approach has potential to serve as a cheap dementia screening, possibly even in non-clinical settings.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信