Continuous Passive Sensing for Bayesian Diagnostics in Mobile Health

现有的医疗体系是被动的，而不是主动的。有限的时间或资金往往迫使人们推迟去看医生。即使人们能够预约，诊所也不堪重负，医生与病人相处的时间也很有限。移动医疗(MHealth)被视为面对面会诊的潜在补充，可以缓解负担过重的医疗系统的压力。MHealth的一个主要方面是使用嵌入在智能手机和智能手表等商用设备中的传感器来测量生物信号并检测症状。这类应用通常分为两类：(1)持续监测人的生理和行为的工具，以及(2)需要与设备进行显式交互以揭示有关症状信息的工具。这些工具通常是独立讨论的，这与临床医生隐含遵循的贝叶斯诊断过程相反。临床医生最初根据患者的病史和自我报告的症状来确定他们的患者患有某种疾病的先验概率，然后使用诊断测试更新该先验概率。指导我的研究计划的中心思想是，通过结合连续的被动感知可以提高诊断测试的效率。例如，如果用户智能手机上的麦克风检测到比平时更多的咳嗽，或者用户智能手表上的温度传感器检测到发烧，通过自动解释视觉快速诊断测试来诊断流感的智能手机应用程序应该会对阳性结果更有信心。虽然我的工作将受到健康应用的推动，但这项工作的主要贡献将是计算机科学。我的研究计划将在普适计算、人机交互和各种形式的应用传感(例如，信号处理、机器学习、计算机视觉)方面产生新的知识。这些贡献将以两个主要研究流的形式出现：(1)被动行为、症状和生理感觉，以及(2)将上述组成部分结合在一起的概率模型。我的研究计划将创建一个灵活的框架，为人们的健康或福祉提供全面的了解。我的计划将在计算机科学的多个子领域产生新的知识。在无处不在的计算中，我的工作将发现可以通过应用传感解决的新问题。在机器学习和统计学方面，我的工作将推进可解释的多模式模型的技术，并将展示组合多模式数据以形成对人的状态的丰富表示的新方法。最后，在人机交互方面，我的工作将引发未来移动健康干预的以用户为中心的设计考虑。

Existing healthcare systems are reactive rather than proactive. Limited time or finances often force people to postpone visits to their doctor. Even when people are able to make an appointment, clinics are overburdened and doctors have limited time with their patients. Mobile health (mHealth) is being viewed as a potential complement to in-person consultations that can relieve stress on overburdened healthcare systems. One major aspect of mHealth is the use of sensors embedded within commodity devices like smartphones and smartwatches to measure biosignals and detect symptoms. Such applications typically fall into one of two categories: (1) tools that continuously monitor a person's physiology and behaviors, and (2) tools that require explicit interaction with a device to reveal information about a symptom. These tools are often discussed independently, which is contrary to the Bayesian diagnostic process clinicians implicitly follow. Clinicians initially formulate a prior probability that their patients have a medical condition according to their medical history and self-reported symptoms, and that prior is then updated using diagnostic tests. The central idea guiding my research program is that the efficacy of diagnostic tests can be improved by incorporating continuous passive sensing. For example, a smartphone app that diagnoses influenza by automatically interpreting a visual rapid diagnostic test should be more confident in a positive result if the microphone on the user's smartphone has detected more coughing than usual or the temperature sensor on the user's smartwatch detects a fever. Although my work will be motivated by health applications, the primary contributions of this work will be made in computer science. My research program will produce new knowledge in ubiquitous computing, human-computer interaction, and various forms of applied sensing (e.g., signal processing, machine learning, computer vision). These contributions will come in the form of two major streams of research: (1) passive behavior, symptom, and physiological sensing, and (2) probabilistic models for combining the aforementioned components together. My research program will create a flexible framework that provides a holistic understanding of people's health or wellbeing. My program will produce new knowledge in multiple subfields of computer science. In ubiquitous computing, my work will uncover novel problems that can be addressed with applied sensing. In machine learning and statistics, my work will advance techniques for interpretable multimodal models and will demonstrate new ways of combining multimodal data to form a rich representation of a person's state. Lastly, in human-computer interaction, my work will elicit user-centered design considerations for future mHealth interventions.