PDF(Pubmed)
Abstract：
Alzheimer\'s disease and related dementias (ADRD) present a looming public health crisis, affecting roughly 5 million people and 11 % of older adults in the United States. Despite nationwide efforts for timely diagnosis of patients with ADRD, >50 % of them are not diagnosed and unaware of their disease. To address this challenge, we developed ADscreen, an innovative speech-processing based ADRD screening algorithm for the protective identification of patients with ADRD. ADscreen consists of five major components: (i) noise reduction for reducing background noises from the audio-recorded patient speech, (ii) modeling the patient\'s ability in phonetic motor planning using acoustic parameters of the patient\'s voice, (iii) modeling the patient\'s ability in semantic and syntactic levels of language organization using linguistic parameters of the patient speech, (iv) extracting vocal and semantic psycholinguistic cues from the patient speech, and (v) building and evaluating the screening algorithm. To identify important speech parameters (features) associated with ADRD, we used the Joint Mutual Information Maximization (JMIM), an effective feature selection method for high dimensional, small sample size datasets. Modeling the relationship between speech parameters and the outcome variable (presence/absence of ADRD) was conducted using three different machine learning (ML) architectures with the capability of joining informative acoustic and linguistic with contextual word embedding vectors obtained from the DistilBERT (Bidirectional Encoder Representations from Transformers). We evaluated the performance of the ADscreen on an audio-recorded patients\' speech (verbal description) for the Cookie-Theft picture description task, which is publicly available in the dementia databank. The joint fusion of acoustic and linguistic parameters with contextual word embedding vectors of DistilBERT achieved F1-score = 84.64 (standard deviation [std] = ±3.58) and AUC-ROC = 92.53 (std = ±3.34) for training dataset, and F1-score = 89.55 and AUC-ROC = 93.89 for the test dataset. In summary, ADscreen has a strong potential to be integrated with clinical workflow to address the need for an ADRD screening tool so that patients with cognitive impairment can receive appropriate and timely care.
摘要：
阿尔茨海默病和相关痴呆（ADRD）提出了一个迫在眉睫的公共卫生危机，影响了美国大约500万人和11%的老年人。尽管在全国范围内努力及时诊断ADRD患者，>50％的人没有被诊断，也没有意识到他们的疾病。为了应对这一挑战，我们开发了ADscreen,一种创新的基于语音处理的ADRD筛选算法，用于ADRD患者的保护性识别。ADscreen由五个主要组件组成：（i）降噪，用于减少音频录制的患者语音中的背景噪声，（ii）使用患者语音的声学参数对患者的语音运动计划能力进行建模，（iii）使用患者语音的语言参数对患者在语言组织的语义和句法水平上的能力进行建模，（iv）从患者语音中提取语音和语义心理语言线索，和(v)建立和评估筛选算法。要识别与ADRD相关的重要语音参数（特征），我们使用了联合互信息最大化(JMIM)，一种有效的高维特征选择方法，小样本量数据集。使用三种不同的机器学习（ML）架构对语音参数与结果变量（ADRD的存在/不存在）之间的关系进行建模，该架构具有将信息声学和语言与从DistilBERT获得的上下文单词嵌入向量（来自变压器的双向编码器表示）。我们评估了ADscreen在Cookie失窃图片描述任务的音频录制患者语音（口头描述）上的表现，在痴呆症数据库中公开提供。声学和语言参数与DistilBERT的上下文单词嵌入向量的联合融合实现了F1分数=84.64（标准偏差[std]=±3.58）和AUC-ROC=92.53（std=±3.34）的训练数据集，并且对于测试数据集,F1分数=89.55和AUC-ROC=93.89。总之,ADscreen具有与临床工作流程整合的强大潜力，以满足对ADRD筛查工具的需求，从而使认知障碍患者可以得到适当和及时的护理。
