Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis.
Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis.
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自动化的智能家居评估以支持疼痛管理：多种方法分析。
DOI：
10.2196/23943
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发表时间：
2020-11-06
影响因子：
7.4
通讯作者：
Cook DJ
中科院分区：
文献类型：
--
作者：
Fritz RL;Wilson M;Dermody G;Schmitter-Edgecombe M;Cook DJ
疼痛管理不善可能导致物质使用障碍，抑郁症，自杀，健康状况恶化和医疗服务的使用增加。大多数疼痛评估发生在远离患者自然环境的临床环境中。智能家居技术的进步可能允许在家庭环境中观察疼痛。识别人类行为的智能家居可能有助于量化功能性疼痛干扰，从而创造评估疼痛和支持疼痛患者的新方法。该研究旨在确定智能家居是否可以检测与疼痛相关的行为，以进行自动评估并支持对慢性疼痛患者的干预。使用历史环境传感器数据和每周护理评估数据进行了多种方法的二次数据分析，这些数据来自11名独立的老年人，他们在1-2年的智能家居监测中报告了疼痛。使用定性方法来解释27个独特疼痛事件的基于传感器的数据，以支持临床医生指导的机器学习模型训练。使用周期图来计算昼夜节律强度，并使用包含100棵树的随机森林来训练机器学习模型以识别疼痛相关行为。该模型为每个基于传感器的数据段提取了550个行为标记。这些被视为二元分类问题（事件，对照）和回归问题。我们发现了13个临床相关的行为，揭示了6个疼痛相关的行为定性主题。使用临床医生指导的随机森林技术对定量结果进行分类，与使用无临床医生指导的标准异常检测技术相比，该技术的分类准确度为0.70，灵敏度为0.72，特异性为0.69，受试者操作特征曲线下面积为0.756，精确-召回曲线下面积为0.777（达到0.16准确度; P<.001）。回归公式达到中度相关，r=0.42。这种二次数据分析的结果表明，疼痛评估智能家居可以识别与疼痛相关的行为。在开发疼痛评估机器学习模型时，利用临床医生的真实世界知识可以提高模型的性能。一个更大的研究集中在疼痛相关的行为是必要的，以提高和测试模型的性能。
Poorly managed pain can lead to substance use disorders, depression, suicide, worsening health, and increased use of health services. Most pain assessments occur in clinical settings away from patients’ natural environments. Advances in smart home technology may allow observation of pain in the home setting. Smart homes recognizing human behaviors may be useful for quantifying functional pain interference, thereby creating new ways of assessing pain and supporting people living with pain. This study aimed to determine if a smart home can detect pain-related behaviors to perform automated assessment and support intervention for persons with chronic pain. A multiple methods, secondary data analysis was conducted using historic ambient sensor data and weekly nursing assessment data from 11 independent older adults reporting pain across 1-2 years of smart home monitoring. A qualitative approach was used to interpret sensor-based data of 27 unique pain events to support clinician-guided training of a machine learning model. A periodogram was used to calculate circadian rhythm strength, and a random forest containing 100 trees was employed to train a machine learning model to recognize pain-related behaviors. The model extracted 550 behavioral markers for each sensor-based data segment. These were treated as both a binary classification problem (event, control) and a regression problem. We found 13 clinically relevant behaviors, revealing 6 pain-related behavioral qualitative themes. Quantitative results were classified using a clinician-guided random forest technique that yielded a classification accuracy of 0.70, sensitivity of 0.72, specificity of 0.69, area under the receiver operating characteristic curve of 0.756, and area under the precision-recall curve of 0.777 in comparison to using standard anomaly detection techniques without clinician guidance (0.16 accuracy achieved; P<.001). The regression formulation achieved moderate correlation, with r=0.42. Findings of this secondary data analysis reveal that a pain-assessing smart home may recognize pain-related behaviors. Utilizing clinicians’ real-world knowledge when developing pain-assessing machine learning models improves the model’s performance. A larger study focusing on pain-related behaviors is warranted to improve and test model performance.
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DOI：
10.1109/tsmc.2013.2252338
发表时间：
2013-11
期刊：
IEEE transactions on systems, man, and cybernetics. Systems
影响因子：
--
作者：
Dawadi PN;Cook DJ;Schmitter-Edgecombe M
通讯作者：
Schmitter-Edgecombe M
使用智能家庭技术在自然环境中分析活动行为和运动。
影响因子：
7.7
作者：
Cook DJ;Schmitter-Edgecombe M;Dawadi P
通讯作者：
Dawadi P
影响因子：
7.7
作者：
Dawadi PN;Cook DJ;Schmitter-Edgecombe M
通讯作者：
Schmitter-Edgecombe M
影响因子：
3.8
作者：
Loetsch, Joern;Sipila, Reetta;Ultsch, Alfred
通讯作者：
Ultsch, Alfred
DOI：
10.1136/amiajnl-2012-001317
发表时间：
2013-09
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Albright D;Lanfranchi A;Fredriksen A;Styler WF 4th;Warner C;Hwang JD;Choi JD;Dligach D;Nielsen RD;Martin J;Ward W;Palmer M;Savova GK
通讯作者：
Savova GK