Automated semantic relevance as an indicator of cognitive decline: Out-of-sample validation on a large-scale longitudinal dataset.
Automated semantic relevance as an indicator of cognitive decline: Out-of-sample validation on a large-scale longitudinal dataset.
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DOI：
10.1002/dad2.12294
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Mueller K
中科院分区：
文献类型：
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作者：
Stegmann G;Hahn S;Bhandari S;Kawabata K;Shefner J;Duncan CJ;Liss J;Berisha V;Mueller K
我们开发并评估了一种自动提取的认知测量(语义相关性)，使用来自健康和认知受损参与者描述波士顿诊断失语症考试中Cookie Theft图片的自动和手动录音转录。我们描述了基本原理和度量验证。我们在一个数据集上开发了该测量方法，并在一个大型数据库(>；2000个样本)上对其进行了评估，方法是将准确性与手动计算的指标进行比较，并评估其临床相关性。全自动测量准确(r=5.84)，具有中等到良好的信度(类内相关=5.73)，与简易智力状态检查相关，并在其他自动语言特征的背景下改善了匹配(r=6.65)，并随着年龄和认知障碍水平的纵向下降。这项研究证明了使用严格的分析和临床框架来验证语音的自动测量，并将其应用于准确和临床相关的测量。
We developed and evaluated an automatically extracted measure of cognition (semantic relevance) using automated and manual transcripts of audio recordings from healthy and cognitively impaired participants describing the Cookie Theft picture from the Boston Diagnostic Aphasia Examination. We describe the rationale and metric validation. We developed the measure on one dataset and evaluated it on a large database (>2000 samples) by comparing accuracy against a manually calculated metric and evaluating its clinical relevance. The fully automated measure was accurate (r = .84), had moderate to good reliability (intra‐class correlation = .73), correlated with Mini‐Mental State Examination and improved the fit in the context of other automatic language features (r = .65), and longitudinally declined with age and level of cognitive impairment. This study demonstrates the use of a rigorous analytical and clinical framework for validating automatic measures of speech, and applied it to a measure that is accurate and clinically relevant.