CSL-CTEA: a systematic method for evaluating novel intelligent cognitive assessment tools.

IF 3.4 3区 医学 Q1 MEDICAL INFORMATICS Health Information Science and Systems Pub Date : 2025-03-11 eCollection Date: 2025-12-01 DOI:10.1007/s13755-025-00346-6
Aihua Li, Sifan Chen, Jianbing Liu, Ting Chen, Yong Shi
{"title":"CSL-CTEA: a systematic method for evaluating novel intelligent cognitive assessment tools.","authors":"Aihua Li, Sifan Chen, Jianbing Liu, Ting Chen, Yong Shi","doi":"10.1007/s13755-025-00346-6","DOIUrl":null,"url":null,"abstract":"<p><p>With the intensification of global population aging, the incidence of cognitive disorders such as dementia continues to rise. The Mini-Mental State Examination (MMSE) and other alternative tools can help doctors detect subtle changes in cognitive function at an early stage. These assessment tools can make a diagnosis before symptoms become severe, providing opportunities for early intervention, which is crucial for delaying disease progression and improving the quality of life of patients. However, traditional cognitive assessment methods are overly complex and affected by various factors. With the development of artificial intelligence technology, many new assessment tools are constantly being developed and improved. How to evaluate the effectiveness of intelligent electronic cognitive assessment tools is particularly important. We have proposed the Correlation and Supervised Learning-based Cognitive Tool Effectiveness Assessment Method (CSL-CTEA) to evaluate the effectiveness of intelligent electronic cognitive assessment tools, including: (1) experimental design and data collection based on traditional scales and intelligent electronic assessment tools, (2) consistency and correlation tests; (3) accuracy analysis of assessment results based on supervised learning. We used CSL-CTEA to explore the effectiveness of a certain electronic assessment. This intelligent electronic cognitive assessment tool includes voice tests, orientation tests, and picture recognition tests to assess cognitive abilities from multiple perspectives. The results show that the electronic assessment is in good agreement with traditional cognitive assessment methods. The various indicators of the electronic assessment can explain the changes in MMSE scores to some extent. The study also found that the electronic assessment performs well in determining whether the subject is at cognitive risk. To some extent, the electronic assessment can replace traditional cognitive assessment methods such as MMSE to help people judge whether they are at risk of cognitive decline.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"13 1","pages":"29"},"PeriodicalIF":3.4000,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11896962/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Information Science and Systems","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s13755-025-00346-6","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/12/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
引用次数: 0

Abstract

With the intensification of global population aging, the incidence of cognitive disorders such as dementia continues to rise. The Mini-Mental State Examination (MMSE) and other alternative tools can help doctors detect subtle changes in cognitive function at an early stage. These assessment tools can make a diagnosis before symptoms become severe, providing opportunities for early intervention, which is crucial for delaying disease progression and improving the quality of life of patients. However, traditional cognitive assessment methods are overly complex and affected by various factors. With the development of artificial intelligence technology, many new assessment tools are constantly being developed and improved. How to evaluate the effectiveness of intelligent electronic cognitive assessment tools is particularly important. We have proposed the Correlation and Supervised Learning-based Cognitive Tool Effectiveness Assessment Method (CSL-CTEA) to evaluate the effectiveness of intelligent electronic cognitive assessment tools, including: (1) experimental design and data collection based on traditional scales and intelligent electronic assessment tools, (2) consistency and correlation tests; (3) accuracy analysis of assessment results based on supervised learning. We used CSL-CTEA to explore the effectiveness of a certain electronic assessment. This intelligent electronic cognitive assessment tool includes voice tests, orientation tests, and picture recognition tests to assess cognitive abilities from multiple perspectives. The results show that the electronic assessment is in good agreement with traditional cognitive assessment methods. The various indicators of the electronic assessment can explain the changes in MMSE scores to some extent. The study also found that the electronic assessment performs well in determining whether the subject is at cognitive risk. To some extent, the electronic assessment can replace traditional cognitive assessment methods such as MMSE to help people judge whether they are at risk of cognitive decline.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
CSL-CTEA:一种评价新型智能认知评估工具的系统方法。
随着全球人口老龄化的加剧,痴呆症等认知障碍的发病率不断上升。迷你精神状态检查(MMSE)和其他替代工具可以帮助医生在早期发现认知功能的细微变化。这些评估工具可以在症状变得严重之前做出诊断,为早期干预提供机会,这对于延缓疾病进展和改善患者的生活质量至关重要。然而,传统的认知评估方法过于复杂,且受多种因素的影响。随着人工智能技术的发展,许多新的评估工具不断被开发和完善。如何评估智能电子认知评估工具的有效性尤为重要。为了评估智能电子认知评估工具的有效性,我们提出了基于相关性和监督学习的认知工具有效性评估方法(CSL-CTEA),包括:(1)基于传统量表和智能电子评估工具的实验设计和数据收集;(2)一致性和相关性测试;(3)基于监督学习的评估结果准确性分析。我们使用CSL-CTEA来探讨某种电子评估的有效性。该智能电子认知评估工具包括语音测试、定向测试和图像识别测试,从多个角度评估认知能力。结果表明,电子评估与传统的认知评估方法有较好的一致性。电子评估的各项指标可以在一定程度上解释MMSE分数的变化。研究还发现,电子评估在确定受试者是否存在认知风险方面表现良好。在一定程度上,电子评估可以取代传统的认知评估方法,如MMSE,帮助人们判断自己是否有认知能力下降的风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
11.30
自引率
5.00%
发文量
30
期刊介绍: Health Information Science and Systems is a multidisciplinary journal that integrates artificial intelligence/computer science/information technology with health science and services, embracing information science research coupled with topics related to the modeling, design, development, integration and management of health information systems, smart health, artificial intelligence in medicine, and computer aided diagnosis, medical expert systems. The scope includes: i.) smart health, artificial Intelligence in medicine, computer aided diagnosis, medical image processing, medical expert systems ii.) medical big data, medical/health/biomedicine information resources such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, optimize the use of information in the health domain, iii.) data management, data mining, and knowledge discovery, all of which play a key role in decision making, management of public health, examination of standards, privacy and security issues, iv.) development of new architectures and applications for health information systems.
期刊最新文献
DiabeRules: a transparent rule based expert system for managing diabetes. Conformal uncertainty quantification to evaluate predictive fairness of foundation AI model for skin lesion classes across patient demographics. Toward intelligent clinical support for personalized sport training rehabilitation via large language models. A vision-language model-based approach for lung cancer diagnosis using lossless 3D CT images: evaluation of GPT-4.1 and GPT-4o for patient-level malignancy assessment. DA3-LUNGNET: a multi-stage deep framework with adaptive attention for early detection of subcentimeter pulmonary nodules.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1