Tang Fengyi, Chen Jun, Dodge Hiroko H, Zhou Jiayu
Department of Computer Science of Engineering, Michigan State University, East Lansing, MI, United States.
Department of Bioinformatics, University of Michigan, Ann Arbor, MI, United States.
Front Digit Health. 2022 Feb 11;3:702772. doi: 10.3389/fdgth.2021.702772. eCollection 2021.
In recent years, behavioral markers such as spoken language and lexical preferences have been studied in the early detection of mild cognitive impairment (MCI) using conversations. While the combination of linguistic and acoustic signals have been shown to be effective in detecting MCI, they have generally been restricted to structured conversations in which the interviewee responds to fixed prompts. In this study, we show that linguistic and acoustic features can be combined synergistically to identify MCI in semi-structured conversations. Using conversational data from an on-going clinical trial (Clinicaltrials.gov: NCT02871921), we find that the combination of linguistic and acoustic features on semi-structured conversations achieves a mean AUC of 82.7, significantly ( < 0.01) out-performing linguistic-only (74.9 mean AUC) or acoustic-only (65.0 mean AUC) detections on hold-out data. Additionally, features (linguistic, acoustic and combination) obtained from semi-structured conversations outperform their counterparts obtained from structured weekly conversations in identifying MCI. Some linguistic categories are significantly better at predicting MCI status (e.g., death, home) than others.
近年来，诸如口语和词汇偏好等行为标志物已被用于通过对话对轻度认知障碍（MCI）进行早期检测的研究中。虽然语言和声学信号的组合已被证明在检测MCI方面是有效的，但它们通常仅限于结构化对话，即受访者对固定提示做出回应的对话。在本研究中，我们表明语言和声学特征可以协同组合以在半结构化对话中识别MCI。使用来自一项正在进行的临床试验（Clinicaltrials.gov：NCT02871921）的对话数据，我们发现半结构化对话中语言和声学特征的组合在留出数据上实现了82.7的平均AUC，显著（<0.01）优于仅语言（平均AUC为74.9）或仅声学（平均AUC为65.0）检测。此外，从半结构化对话中获得的特征（语言、声学和组合）在识别MCI方面优于从结构化每周对话中获得的对应特征。一些语言类别在预测MCI状态方面（例如，死亡、家庭）比其他类别显著更好。
