基于自然语言处理的语音记录分析技术在轻度认知障碍和痴呆症中的自动检测
Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach.
机构信息
Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA.
Framingham Heart Study, Boston University, Boston, Massachusetts, USA.
出版信息
Alzheimers Dement. 2023 Mar;19(3):946-955. doi: 10.1002/alz.12721. Epub 2022 Jul 7.
INTRODUCTION
Automated computational assessment of neuropsychological tests would enable widespread, cost-effective screening for dementia.
METHODS
A novel natural language processing approach is developed and validated to identify different stages of dementia based on automated transcription of digital voice recordings of subjects' neuropsychological tests conducted by the Framingham Heart Study (n = 1084). Transcribed sentences from the test were encoded into quantitative data and several models were trained and tested using these data and the participants' demographic characteristics.
RESULTS
Average area under the curve (AUC) on the held-out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal cognition from Dementia, Normal or Mild Cognitive Impairment (MCI) from Dementia, and Normal from MCI, respectively.
DISCUSSION
The proposed approach offers a fully automated identification of MCI and dementia based on a recorded neuropsychological test, providing an opportunity to develop a remote screening tool that could be adapted easily to any language.
简介
自动化的计算评估神经心理学测试将能够实现广泛、具有成本效益的痴呆症筛查。
方法
开发并验证了一种新的自然语言处理方法，该方法基于弗雷明汉心脏研究（Framingham Heart Study，n=1084）中对受试者神经心理学测试的数字语音记录的自动转录，来识别不同阶段的痴呆症。测试中的转录句子被编码为定量数据，然后使用这些数据和参与者的人口统计学特征来训练和测试多个模型。
结果
在保留的测试数据上，平均曲线下面积（AUC）分别达到 92.6%、88.0%和 74.4%，用于区分正常认知与痴呆、正常或轻度认知障碍（MCI）与痴呆、正常与 MCI。
讨论
该方法提供了一种基于记录的神经心理学测试的 MCI 和痴呆的全自动识别方法，为开发一种远程筛查工具提供了机会，该工具可以很容易地适应任何语言。
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