利用多模态传感器和机器学习对社区居住痴呆患者进行早期激越预测的基准评测
Benchmarking Early Agitation Prediction in Community-Dwelling People with Dementia Using Multimodal Sensors and Machine Learning
AI总结：
本文基于TIHM多模态居家传感器数据，构建并基准评测社区痴呆患者激越早期预测模型，融合活动情境特征后LightGBM取得AUC-ROC 0.9720。
摘要：
激越是痴呆患者最常见的反应性行为之一，尤其见于居住在社区环境、没有持续临床监护的人群。及时预测激越可实现早期干预、减轻照护者负担，并改善患者与照护者双方的生活质量。本研究旨在利用多模态传感器数据，开发并基准评测用于社区居住老年痴呆患者激越早期预测的机器学习方法。研究引入了一组从活动数据中导出的、与激越相关的新情境特征，并将其用于激越预测。研究在多种问题表述下评估了广泛的机器学习和深度学习模型，包括针对单时间戳表格传感器数据和多时间戳序列传感器数据的二分类，以及针对单时间戳表格传感器数据的异常检测。研究使用了 Technology Integrated Health Management（TIHM）数据集，这是用于痴呆患者远程监测的最大公开数据集，包含2,803天的居家活动、生理和睡眠数据。最有效的设置是利用当前6小时时间戳的传感器数据进行二分类，以预测下一时刻的激越。纳入一天中的时间和激越史等附加信息进一步提升了模型性能，其中 light gradient boosting machine 取得了最高 AUC-ROC 0.9720 和 AUC-PR 0.4320。本工作首次对使用隐私保护型传感器数据在社区痴呆照护中进行激越预测的最先进技术进行了全面基准评测。该方法能够实现准确、可解释且高效的激越预测，为主动式痴呆照护和原地养老提供支持。
英文摘要：
Agitation is one of the most common responsive behaviors in people living with dementia, particularly among those residing in community settings without continuous clinical supervision. Timely prediction of agitation can enable early intervention, reduce caregiver burden, and improve the quality of life for both patients and caregivers. This study aimed to develop and benchmark machine learning approaches for the early prediction of agitation in community-dwelling older adults with dementia using multimodal sensor data. A new set of agitation-related contextual features derived from activity data was introduced and employed for agitation prediction. A wide range of machine learning and deep learning models was evaluated across multiple problem formulations, including binary classification for single-timestamp tabular sensor data and multi-timestamp sequential sensor data, as well as anomaly detection for single-timestamp tabular sensor data. The study utilized the Technology Integrated Health Management (TIHM) dataset, the largest publicly available dataset for remote monitoring of people living with dementia, comprising 2,803 days of in-home activity, physiology, and sleep data. The most effective setting involved binary classification of sensor data using the current 6-hour timestamp to predict agitation at the subsequent timestamp. Incorporating additional information, such as time of day and agitation history, further improved model performance, with the highest AUC-ROC of 0.9720 and AUC-PR of 0.4320 achieved by the light gradient boosting machine. This work presents the first comprehensive benchmarking of state-of-the-art techniques for agitation prediction in community-based dementia care using privacy-preserving sensor data. The approach enables accurate, explainable, and efficient agitation prediction, supporting proactive dementia care and aging in place.
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。
