ADscreen:基于语音处理的选系统,用于自动识别患有阿尔茨海默病和相关痴呆症的患者
Maryam Zolnoori1, Ali Zolnour2, Maxim Topaz1
1Columbia University Medical Center, New York, NY, United States of America; School of Nursing, Columbia University, New York, NY, United States of America.
Artificial intelligence in medicine
|September 6, 2023
概括
一个新的语音处理算法,ADscreen,有助于早期发现阿尔茨海默病和相关痴呆症 (ADRD). 该工具分析语音模式,以识别可能有未被诊断的认知障碍的人,从而促进及时的护理.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 语音处理 语音处理
背景情况:
- 阿尔茨海默病和相关痴呆症 (ADRD) 影响数百万,在美国,超过50%的病例未被诊断出来.
- 及时诊断对于有效的患者管理和护理至关重要.
- 当前的诊断方法在广泛的早期查方面面临挑战.
研究的目的:
- 开发和评估ADscreen,一个用于早期ADRD查的创新语音处理算法.
- 识别关键的声学和语言语音特征,表明ADRD.
- 将语音分析与先进的机器学习相结合,以提高诊断准确度.
主要方法:
- 开发了ADscreen,这是一个五个组成部分的算法,包括降噪,声学和语言参数建模,以及心理语言暗示提取.
- 利用联合互助信息最大化 (JMIM) 在高维数据中进行特征选择.
- 采用了机器学习架构,将声学,语言和DistilBERT词嵌入结合起来,用于ADRD预测.
主要成果:
- 在识别ADRD时,ADscreen算法表现出高性能.
- 在测试数据集上获得了F1得分89.55和AUC-ROC93.89.
- 声学,语言和DistilBERT特征的联合融合产生了最好的结果.
结论:
- ADscreen显示出作为ADRD的非侵入性查工具的巨大潜力.
- 该算法可以集成到临床工作流程中,以帮助早期识别认知障碍.
- 便于及时干预和照顾ADRD患者.
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