欠采样和累积类重新决策方法，以改善痴呆症患者躁动的检测。Undersampling and cumulative class re-decision methods to improve detection of agitation in people with dementia.-医云文献数字医云科研云海量医学决策数据服务

Abstract：

Agitation is one of the most prevalent symptoms in people with dementia (PwD) that can place themselves and the caregiver\'s safety at risk. Developing objective agitation detection approaches is important to support health and safety of PwD living in a residential setting. In a previous study, we collected multimodal wearable sensor data from 17 participants for 600 days and developed machine learning models for detecting agitation in 1-min windows. However, there are significant limitations in the dataset, such as imbalance problem and potential imprecise labels as the occurrence of agitation is much rarer in comparison to the normal behaviours. In this paper, we first implemented different undersampling methods to eliminate the imbalance problem, and came to the conclusion that only 20% of normal behaviour data were adequate to train a competitive agitation detection model. Then, we designed a weighted undersampling method to evaluate the manual labeling mechanism given the ambiguous time interval assumption. After that, the postprocessing method of cumulative class re-decision (CCR) was proposed based on the historical sequential information and continuity characteristic of agitation, improving the decision-making performance for the potential application of agitation detection system. The results showed that a combination of undersampling and CCR improved F1-score and other metrics to varying degrees with less training time and data.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s13534-023-00313-8.

摘要：

躁动是痴呆症（PwD）患者最常见的症状之一，可能会使自己和护理人员的安全处于危险之中。开发客观的激动检测方法对于支持居住在住宅环境中的PwD的健康和安全非常重要。在之前的研究中,我们收集了17名参与者600天的多模态可穿戴传感器数据,并开发了用于在1分钟窗口内检测躁动的机器学习模型.然而,数据集中有很大的限制，例如失衡问题和潜在的不精确标签，因为与正常行为相比，躁动的发生要罕见得多。在本文中,我们首先实施了不同的欠采样方法来消除不平衡问题，得出的结论是，只有20％的正常行为数据足以训练竞争性躁动检测模型。然后，我们设计了一种加权欠采样方法来评估人工标记机制给定模糊的时间间隔假设。之后,基于历史序列信息和搅拌的连续性特征，提出了累积类再决策(CCR)的后处理方法，提高搅拌检测系统潜在应用的决策性能。结果表明，欠采样和CCR相结合，以较少的训练时间和数据，不同程度地提高了F1分数和其他指标。
■在线版本包含补充材料，可在10.1007/s13534-023-00313-8获得。