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Sensemaking for mobile health

机译：移动健康感

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Mobile health (mHealth) leverages the power and ubiquity of mobile and cloud technologies to support patients and clinicians in monitoring and understanding symptoms, side effects and treatment outside the clinical setting; thereby closing the feedback loops of self-care, clinical-care, and personal-evidence-creation. However, to realize this promise, we must develop new data capture, processing and modeling techniques to convert the digital exhaust emitted by mobile phone use into behavioral biomarkers. This calls for a modular layered sensemaking framework in which low level state classifications of raw data (e.g., estimated activity states such as sitting, walking, driving from continuous accelerometer and location traces), are used to derive mid-level semantic features (e.g., total number of ambulatory minutes, number of hours spent out of house), that can then be mapped to particular behavioral biomarkers for specific diseases (e.g., chronic pain, GI disfunction, MS, fatigue, depression, etc). The techniques needed to derive these markers will range from simple functions to machine learning classifiers, and will need to fuse diverse data types, but all will need to cope with noisy, erratic data sources. We are working to build an open architecture and community to speed the rate and robustness of innovation in this space, both academic and commercial (http://openmhealth.org).
机译：移动健康（mHealth）利用移动和云技术的强大功能和普遍性来支持患者和临床医生监视和了解临床环境以外的症状，副作用和治疗方法；从而封闭了自我护理，临床护理和个人证据创造的反馈循环。但是，要实现这一诺言，我们必须开发新的数据捕获，处理和建模技术，以将手机使用产生的数字尾气转化为行为生物标记。这就需要一个模块化的分层感官框架，在该框架中，原始数据的低级状态分类（例如，估计的活动状态，如坐着，走路，从连续加速度计行驶和位置跟踪）将用于导出中级语义特征（例如，总的门诊时间总数，出门服务时间），然后可以将其映射到特定疾病（例如慢性疼痛，胃肠功能障碍，MS，疲劳，抑郁等）的特定行为生物标记。导出这些标记所需的技术范围从简单的功能到机器学习分类器，将需要融合各种数据类型，但是所有这些都将需要应对嘈杂，不稳定的数据源。我们正在努力建立一个开放的体系结构和社区，以加快该领域（无论是学术领域还是商业领域）创新的速度和健壮性（http://openmhealth.org）。

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