SCH: INT: Collaborative Research: Novel Computational Methods for Continuous Objective Multimodal Pain Assessment Sensing System (COMPASS)

目前很少有客观的疼痛评估技术可用于临床设置。临床医生通常使用主观疼痛量表来评估和管理疼痛，这导致治疗计划不理想，对患者需求的反应迟缓，阿片类药物的过度处方以及患者的药物寻求行为。本项目将研究基于科学的方法来构建稳健的连续客观多模态疼痛评估传感系统（COMPASS）和临床接口，能够从多模态生理信号和面部表情中产生客观的疼痛测量。COMPASS将允许客观测量，可用于显着改善疼痛评估，疼痛管理策略，减少阿片类药物依赖，并推进疼痛相关研究领域。该教育计划将包括参与患者培训、K-12学生、少数民族和代表性不足群体以及公众的活动。这些成果还将导致发展支持先进医疗技术和服务所需的多样化劳动力。利用先进的生物传感系统、数据融合算法和机器学习模型，该项目将开发一个强大、可靠、准确的疼痛强度分类系统COMPASS，用于在0-10级的范围内实时估计患者的疼痛强度，这是医生在临床环境中使用的标准等级。在项目的初始阶段，该团队将在布莱根妇女医院进行试点，试验开发传感系统的不同元素，并收集数据以开发数据融合算法和机器学习模型。在项目的后期阶段，团队将收集大量数据来训练和验证完全实施的COMPASS。当受试者经历由引起患者疼痛的药物疗法调节的疼痛时，将收集来自脑电图、面部表情、患者自述疼痛量表和医生/护士评估的疼痛量表的生理传感器数据。该项目将研究基于证据的机器学习和生理信号和面部表情图像的特征提取方法。这项高度跨学科的研究将对疼痛评估和管理、人为因素和患者安全等领域做出重大贡献。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Few objective pain assessment techniques are currently available for use in clinical settings. Clinicians typically use subjective pain scales for pain assessment and management, which has resulted in suboptimal treatment plans, delayed responses to patient needs, over-prescription of opioids, and drug-seeking behavior among patients. This project will investigate science-based methods to build a robust Continuous Objective Multimodal Pain Assessment Sensing System (COMPASS) and a clinical interface capable of generating objective measurements of pain from multimodal physiological signals and facial expressions. COMPASS will allow objective measurements that can be used to significantly improve pain assessment, pain management strategies, reduce opioid dependency, and advance the field of pain-related research. The educational plan will include activities to engage patient training, K-12 students, minorities and underrepresented groups, as well as general public. These outcomes will also lead to development of a diverse work force needed to support advanced medical technologies and services.Using advanced biosensing systems, data fusion algorithms and machine learning models, this project will develop a robust, reliable, and accurate pain intensity classification system, COMPASS, for estimating pain intensity experienced by patients in real-time on a 0-10 scale, which is the standard scale used by physicians in clinical settings. In the initial phase of the project, the team will conduct a pilot at Brigham and Women's Hospital to experiment with the different elements for developing the sensing systems and collect data to develop data fusion algorithms and machine learning models. In the later phase of the project, the team will collect an extensive set of data to train and validate the fully implemented COMPASS. Physiological sensor data from electroencephalograph, facial-expression, patient self-reported pain scales, and physician/nurse assessed pain scales will be collected from the subjects as they experience pain modulated by medical therapies that cause patients pain. The project will investigate evidence-based machine learning and feature extraction methods for physiological signals and facial-expression images. This highly interdisciplinary research will make significant contributions to the areas of pain assessment and management, human factors and patient safety.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.