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首页> 外文期刊>EPJ Data Science >Connecting and linking neurocognitive, digital phenotyping, physiologic, psychophysical, neuroimaging, genomic,sensor data with survey data
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Connecting and linking neurocognitive, digital phenotyping, physiologic, psychophysical, neuroimaging, genomic,sensor data with survey data

机译:连接和连接神经认知,数字表型,生理,心理物理,神经影像,基因组,传感器数据,具有调查数据

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Combining survey data with alternative data sources (e.g., wearable technology, apps, physiological, ecological monitoring, genomic, neurocognitive assessments, brain imaging, and psychophysical data) to paint a complete biobehavioral picture of trauma patients comes with many complex system challenges and solutions. Starting in emergency departments and incorporating these diverse, broad, and separate data streams presents technical, operational, and logistical challenges but allows for a greater scientific understanding of the long-term effects of trauma. Our manuscript describes incorporating and prospectively linking these multi-dimensional big data elements into a clinical, observational study at US emergency departments with the goal to understand, prevent, and predict adverse posttraumatic neuropsychiatric sequelae (APNS) that affects over 40 million Americans annually. We outline key data-driven system challenges and solutions and investigate eligibility considerations, compliance, and response rate outcomes incorporating these diverse “big data” measures using integrated data-driven cross-discipline system architecture.
机译:将调查数据与替代数据来源相结合(例如,可穿戴技术,应用,生理学,生态监测,基因组,神经认知评估,脑成像和心理物理数据)来绘制创伤患者的完整生物侵蚀图像,具有许多复杂的系统挑战和解决方案。从急诊部门开始并将这些不同,广泛和独立的数据流纳入技术,运营和后勤挑战,但允许更大的科学理解创伤的长期影响。我们的稿件描述了将这些多维大数据元素的临床,观测研究纳入美国急诊部门的临床,观测研究,目标是理解,预防和预测每年为超过4000万美国人影响超过4000万美国人的不利后期的术后性神经精神病因(APN)。我们概述了关键数据驱动的系统挑战和解决方案,并调查使用集成数据驱动的跨学科系统架构的资格考虑,遵守性和响应率结果,包括这些不同的“大数据”措施。

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