Characterizing and detecting delirium with clinical and computational measures of speech and language disturbance.
Characterizing and detecting delirium with clinical and computational measures of speech and language disturbance.
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DOI：
10.1503/jpn.230026
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发表时间：
2023-07
影响因子：
4.3
通讯作者：
Sinvani, Liron
中科院分区：
文献类型：
--
作者：
Tang, Sunny X. X.;Cong, Yan;Mercep, Gwenyth;Bhatti, Mutahira;Serpe, Grace;Gromova, Valeria;Berretta, Sarah;John, Majnu;Liberman, Mark Y.;Sinvani, Liron
谵妄是一种严重诊断不足的精神状态改变综合征，影响超过50%的老年人入院治疗。很少有研究将言语和语言障碍纳入谵妄检测。我们试图描述谵妄中的言语和语言障碍，并提供一个使用计算言语和语言特征检测谵妄的概念证明。参与者接受了谵妄评估并完成了语言任务。使用标准化临床量表评定言语和语言障碍。使用自动化管道处理录音和抄本，以提取声学和文本特征。我们使用二项式，弹性网络，机器学习模型来预测谵妄状态。我们纳入了33名住院的老年人，其中10人符合谵妄的标准。谵妄组在总体语言障碍和不连贯性方面得分较高，在类别流畅性方面得分较低。两组在类别流畅性上的得分都低于正常人群。认知功能障碍作为一个连续的措施与较高的总语言障碍，不连贯，目标的损失和较低的类别流畅性。在模型中加入计算语言特征，预测谵妄状态的准确率提高到78%。这是一项样本量有限的概念验证研究，没有预留交叉验证样本。在建立检测谵妄的可推广模型之前，需要进行后续研究。语言障碍在谵妄患者中升高，也可用于识别阈下认知障碍。计算语音和语言特征是有前途的准确，非侵入性和有效的生物标志物谵妄。
Delirium is a critically underdiagnosed syndrome of altered mental status affecting more than 50% of older adults admitted to hospital. Few studies have incorporated speech and language disturbance in delirium detection. We sought to describe speech and language disturbances in delirium, and provide a proof of concept for detecting delirium using computational speech and language features. Participants underwent delirium assessment and completed language tasks. Speech and language disturbances were rated using standardized clinical scales. Recordings and transcripts were processed using an automated pipeline to extract acoustic and textual features. We used binomial, elastic net, machine learning models to predict delirium status. We included 33 older adults admitted to hospital, of whom 10 met criteria for delirium. The group with delirium scored higher on total language disturbances and incoherence, and lower on category fluency. Both groups scored lower on category fluency than the normative population. Cognitive dysfunction as a continuous measure was correlated with higher total language disturbance, incoherence, loss of goal and lower category fluency. Including computational language features in the model predicting delirium status increased accuracy to 78%. This was a proof-of-concept study with limited sample size, without a set-aside cross-validation sample. Subsequent studies are needed before establishing a generalizable model for detecting delirium. Language impairments were elevated among patients with delirium and may also be used to identify subthreshold cognitive disturbances. Computational speech and language features are promising as accurate, noninvasive and efficient biomarkers of delirium.
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影响因子：
39.2
作者：
Inouye SK;Kosar CM;Tommet D;Schmitt EM;Puelle MR;Saczynski JS;Marcantonio ER;Jones RN
通讯作者：
Jones RN
影响因子：
5.2
作者：
Liu, Ning;Luo, Kexue;Yuan, Zhenming;Chen, Yan
通讯作者：
Chen, Yan
影响因子：
4.8
作者：
Lindsay H;Tröger J;König A
通讯作者：
König A
影响因子：
168.9
作者：
Inouye, Sharon K.;Westendorp, Rudi G. J.;Saczynski, Jane S.
通讯作者：
Saczynski, Jane S.
影响因子：
10.5
作者：
Liddle, PF;Ngan, ETC;Weg, R
通讯作者：
Weg, R