English(EN) Explainable Prediction from Mobile Sensing Data through LLM-guided Concept Integration

LLM引导的概念集成提高了移动传感预测的准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

研究人员开发了一种名为概念集成Transformer（CIT）的新模型，该模型利用大型语言模型（LLM）来提高移动传感研究中的预测准确性和可解释性。该方法生成具有置信度权重的概念异常目标，无需手动标注。在两个数据集上的测试中，CIT在情感预测方面取得了高F1分数，并在PHQ-9数据集上并列最高分，同时还揭示了与睡眠数量和质量相关的可解释模式。 AI

影响这项研究可能导致使用日常移动设备进行更准确、更可解释的健康监测工具。

排序理由这是一篇详细介绍新模型和方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Yuning Wang, Iman Azimi, Amir M. Rahmani, Pasi Liljeberg · 2026-09-14 04:00

通过LLM引导的概念整合从移动传感数据中进行可解释的预测

arXiv:2609.11995v1 Announce Type: new Abstract: Mobile sensing enables longitudinal monitoring of behavioral and physiological patterns in everyday settings. However, accurate prediction remains challenging in small-cohort health-sensing studies, where task-specific outcome super…

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通过LLM引导的概念整合从移动传感数据中进行可解释的预测

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