用大模型分析老人访谈语音，区分抑郁和痴呆严重程度。
Reading between the Lines: Leveraging Large Language Models for Global Dementia and Depression Assessment from Clinical Interviews
- 用大模型直接预测情绪与认知症状，无需额外标注数据。
- 抑郁评估零样本误差仅0.60，痴呆通过特征提取误差降35%。
- 自动转录加停顿信息即可达人工水平，适合筛查应用。
痴呆与抑郁是老年人中最常见的神经精神障碍，其症状重叠给鉴别诊断带来巨大挑战。本研究探索开放权重的大语言模型（LLMs）从154名德语使用者标准化病史访谈的语音中预测痴呆与抑郁严重程度。我们提出一种基于观察者的全球抑郁量表（GDS-D），与已有全球衰退量表（GDS）对齐，实现情绪与认知症状的同步全局分期。对比三种LLM（Mistral 3.1、DeepHermes、Qwen3）在两种设置下的表现：（1）零样本预测；（2）基于LLM的特征提取结合支持向量回归，使用人工转录与含停顿信息的转录。结果显示，零样本下大模型可有效预测抑郁严重程度（最佳平均绝对误差MAE为0.60）；而痴呆评估则显著受益于结构化特征提取（最佳MAE为0.78），相比零样本基线错误降低最多达35%。含停顿信息的自动转录性能媲美人工转录，证明全自动筛查流程在神经精神疾病鉴别中的可行性。
原文摘要 · Abstract (English)
Dementia and depression are the most prevalent neuropsychiatric disorders in geriatric populations, and their overlapping symptoms pose major challenges for differential diagnosis. In this study, we investigate open-weights Large Language Models (LLMs) for predicting dementia and depression severity from speech samples collected during standardized history taking interviews with 154 German-speaking subjects. We introduce an observer-based Global Depression Scale (GDS-D) aligned with the established Global Deterioration Scale (GDS), enabling parallel global staging of affective and cognitive symptoms. We compare three LLMs (Mistral 3.1, DeepHermes, Qwen3) in two settings: (1) zero-shot prediction and (2) LLM-based feature extraction for Support Vector Regression, using human and pause-enriched transcripts. Results show that LLMs effectively predict depression severity in zero-shot settings (best MAE of 0.60), while dementia assessment benefits substantially from structured feature extraction (best MAE of 0.78), reducing errors by up to 35% over zero-shot baselines. Pause-enriched transcripts achieve competitive performance with human transcriptions, demonstrating the viability of fully automatic screening pipelines for differential neuropsychiatric assessment.
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