A Systematic Review of Natural Language Processing Techniques for Early Detection of Cognitive Impairment

Ravi Shankar PhD , Anjali Bundele MPH , Amartya Mukhopadhyay FRCP
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引用次数: 0

Abstract

Objective

To systematically evaluate the effectiveness and methodologic approaches of natural language processing (NLP) techniques for early detection of cognitive decline through speech and language analysis.

Methods

We conducted a comprehensive search of 8 databases from inception through August 31, 2024, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies were included if they used NLP techniques to analyze speech or language data for detecting cognitive impairment and reported diagnostic accuracy metrics. Two independent reviewers (R.S. and A.B.) screened articles and extracted data on study characteristics, NLP methods, and outcomes.

Results

Of 23,562 records identified, 51 studies met inclusion criteria, involving 17,340 participants (mean age, 72.4 years). Combined linguistic and acoustic approaches achieved the highest diagnostic accuracy (average 87%; area under the curve [AUC], 0.89) compared with linguistic-only (83%; AUC, 0.85) or acoustic-only approaches (80%; AUC, 0.82). Lexical diversity, syntactic complexity, and semantic coherence were consistently strong predictors across cognitive conditions. Picture description tasks were most common (n=21), followed by spontaneous speech (n=15) and story recall (n=8). Crosslinguistic applicability was found across 8 languages, although language-specific adaptations were necessary. Longitudinal studies (n=9) reported potential for early detection but were limited by smaller sample sizes (average n=159) compared with cross-sectional studies (n=42; average n=274).

Conclusion

Natural language processing techniques show promising diagnostic accuracy for detecting cognitive impairment across multiple languages and clinical contexts. Although combined linguistic-acoustic approaches appear most effective, methodologic heterogeneity and small sample sizes in existing studies suggest the need for larger, standardized investigations to establish clinical utility.
自然语言处理技术在认知障碍早期检测中的系统综述
目的系统评价自然语言处理(NLP)技术在语音和语言分析中早期发现认知衰退的有效性和方法方法。方法:我们按照系统评价和meta分析指南的首选报告项目,从研究开始到2024年8月31日,对8个数据库进行了全面检索。如果研究使用NLP技术来分析语音或语言数据以检测认知障碍并报告诊断准确性指标,则将其纳入研究。两位独立审稿人(R.S.和A.B.)筛选了文章并提取了研究特征、NLP方法和结果的数据。结果在确定的23,562份记录中,51项研究符合纳入标准,涉及17,340名参与者(平均年龄72.4岁)。语言和声学相结合的方法达到了最高的诊断准确率(平均87%;曲线下面积[AUC], 0.89)与仅语言(83%;AUC, 0.85)或纯声学方法(80%;AUC, 0.82)。词汇多样性、句法复杂性和语义一致性在认知条件下始终是强有力的预测因子。图片描述任务最常见(n=21),其次是自发演讲(n=15)和故事回忆(n=8)。在8种语言中发现了跨语言适用性,尽管语言特定的适应性是必要的。纵向研究(n=9)报告了早期发现的潜力,但与横断面研究(n=42;平均n = 274)。结论自然语言处理技术在多语言和临床背景下诊断认知障碍具有良好的准确性。虽然结合语言-声学方法似乎是最有效的,但现有研究的方法异质性和小样本量表明需要更大规模的标准化调查来建立临床效用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Mayo Clinic Proceedings. Digital health
Mayo Clinic Proceedings. Digital health Medicine and Dentistry (General), Health Informatics, Public Health and Health Policy
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