痴呆银行皮特语料库中早期认知衰退的语言指标：统计与机器学习研究
Linguistic Indicators of Early Cognitive Decline in the DementiaBank Pitt Corpus: A Statistical and Machine Learning Study
AI总结：
本研究通过统计与机器学习方法，分析痴呆银行皮特语料库中的语言特征，揭示早期认知衰退的可解释语言指标。
摘要：
背景：自发语言生产中的细微变化是认知衰退最早的表现之一。识别出可以解释的语言标记可以帮助建立透明且有临床依据的筛查方法。 方法：本研究分析了来自痴呆银行皮特语料库的自发语音转录文本，使用三种语言表示方法：原始清理文本、结合词法和语法信息的词性标注增强表示，以及仅词性标注的语法表示。使用逻辑回归和随机森林模型，在两种协议下进行评估：转录文本级别的训练-测试分割和受试者级别的五折交叉验证以防止说话人重叠。通过全局特征重要性检查模型的可解释性，并使用曼-惠特尼U检验和Cliff's delta效应大小进行统计验证。 结果：在各种表示方法中，模型表现稳定，即使在没有词汇内容的情况下，语法和语法特征仍保持强大的辨别能力。受试者级别的评估产生了更加保守但一致的结果，特别是对于词性标注增强和词性标注仅有的表示方法。统计分析揭示了功能词使用、词汇多样性、句子结构和话语连贯性在群体间的显著差异，这与机器学习特征重要性发现密切相关。 结论：结果表明，抽象语言特征在临床现实评估下能够捕捉到早期认知衰退的稳健标记。通过结合可解释的机器学习与非参数统计验证，本研究支持了基于语言特征的透明和可靠的语言认知筛查应用。
英文摘要：
Background: Subtle changes in spontaneous language production are among the earliest indicators of cognitive decline. Identifying linguistically interpretable markers of dementia can support transparent and clinically grounded screening approaches. Methods: This study analyzes spontaneous speech transcripts from the DementiaBank Pitt Corpus using three linguistic representations: raw cleaned text, a part-of-speech (POS)-enhanced representation combining lexical and grammatical information, and a POS-only syntactic representation. Logistic regression and random forest models were evaluated under two protocols: transcript-level train-test splits and subject-level five-fold cross-validation to prevent speaker overlap. Model interpretability was examined using global feature importance, and statistical validation was conducted using Mann-Whitney U tests with Cliff's delta effect sizes. Results: Across representations, models achieved stable performance, with syntactic and grammatical features retaining strong discriminative power even in the absence of lexical content. Subject-level evaluation yielded more conservative but consistent results, particularly for POS-enhanced and POS-only representations. Statistical analysis revealed significant group differences in functional word usage, lexical diversity, sentence structure, and discourse coherence, aligning closely with machine learning feature importance findings. Conclusion: The results demonstrate that abstract linguistic features capture robust markers of early cognitive decline under clinically realistic evaluation. By combining interpretable machine learning with non-parametric statistical validation, this study supports the use of linguistically grounded features for transparent and reliable language-based cognitive screening.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
