用多模态数据让大模型自动分析心理状态，提升评估准确性。
Evidence-Grounded Mapping of Multimodal Human Sensing Psychological Transdiagnostic Dimensions
- 构建临床参与的评测基准，验证LLM从多种数据生成心理维度评分的能力。
- 在14,592个样本中，通过语义抽象提升自报告数据的组织效率。
- 适用于心理健康研究者与临床心理学家，尤其关注跨诊断心理维度建模。
移动与可穿戴传感技术实现了对行为的长期监测，但如何将这些信号转化为有意义的心理健康构念仍具挑战。本文引入一种临床医生参与的评测基准，评估大语言模型（LLMs）能否基于被动传感、生态瞬时评估（EMA）和问卷数据生成证据支持的《简明层级病理学分类》（B-HiTOP）条目评分。利用GLOBEM数据集，构建了14,592个参与者-天实例，并将多模态证据映射至5个谱系上的29个B-HiTOP条目。由于GLOBEM缺乏真实B-HiTOP响应，采用证据兼容性（C）作为评估指标，区分有充分证据的预测与因证据不足而放弃的判断。两阶段预测提升了EMA和问卷数据的兼容性，但在被动传感及混合证据下反而降低兼容性，且使各模型、谱系与证据设置下的评分分布更保守。总体而言，语义抽象有助于整合异质自报告数据，却成为间接行为传感信号的信息瓶颈。
原文摘要 · Abstract (English)
Mobile and wearable sensing enables longitudinal observation of behavior, yet translating these signals into meaningful mental health constructs remains difficult. We introduce a clinician-in-the-loop benchmark for evaluating whether large language models (LLMs) can generate evidence-grounded Brief Hierarchical Taxonomy of Psychopathology (B-HiTOP) item profiles from passive sensing, ecological momentary assessment (EMA), and questionnaire evidence. Using the Generalization of Longitudinal Behavior Modeling (GLOBEM) dataset, we construct 14,592 participant-day instances and align multimodal evidence to 29 B-HiTOP items across five spectra. Since GLOBEM lacks B-HiTOP responses, we evaluate evidence compatibility (C) rather than diagnostic accuracy, separating substantive predictions from abstentions when evidence is insufficient for item-level scoring. Two-stage prediction improves C for EMA and questionnaire evidence, but reduces C under passive sensing and combined evidence and produces more conservative score distributions across models, spectra, and evidence settings. Overall, semantic abstraction helps organize heterogeneous self-report evidence while becoming an information bottleneck for indirect behavioral sensing signals.
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