CAREER: New Frontiers in Sequential Decision Making with a View Towards Mobile Health Applications

职业：顺序决策的新领域，着眼于移动医疗应用

• 批准号: 1452099
• 负责人: Ambuj Tewari
• 金额: $ 49.99万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1452099&HistoricalAwards=false
• 关键词: CAREER; New; Frontiers; Sequential; Decision

This NSF CAREER project uses machine learning to advance the science of health interventions delivered via mobile devices. The field of mobile health (mhealth) is advancing rapidly spurred by the widespread use of mobile devices across the world. Mhealth researchers are using mobile devices to diagnose, manage, and treat a variety of health conditions including, but by no means limited to, alcohol abuse, depression, obesity, and smoking. The project creates new learning algorithms that learn to personalize mhealth interventions to the changing needs of individual users. It also develops techniques that use the social network among users to intervene so that the entire network is steered towards positive health outcomes. The project will give undergraduate researchers the opportunity to work on cutting edge data science driven by socially relevant problems. It will develop new graduate level courses on sequential decision making. Software including simulations specific to health domains, implementations of learning algorithms, and mobile apps that deliver interventions will be publicly released. Research results will be communicated to, and feedback solicited from, the general public under the auspices of the newly formed Michigan Institute for Data Science (MIDAS). Involvement of under-represented minorities will be encouraged via the NextProf Science workshop and Faculty Allies for Diversity program at the University of Michigan.The project involves solving three technical challenges. First, existing algorithms for sequential decision making, such as contextual bandit algorithms, need to be redesigned to make them suitable for adoption in mhealth. The project will develop actor-critic contextual bandit algorithms that separate the representation of the policy from the representation of the reward function. Having an interpretable, low dimensional policy space is crucial for interpretability of the learned policy by the domain scientist. Second, most of the literature on bandit problems and reinforcement learning assumes stationarity, an unreasonable assumption in mhealth. Using recent advances in the field of no-regret learning, the project will develop learning algorithms that can deal with non-stationarity. Third, the science of network interventions is in its infancy. The project will serve a catalyst for its development by synthesizing recent advances in high dimensional statistical learning and control of complex networks to design learning algorithms that intervene at a small number of nodes in a time evolving network to achieve a desired long term objective.

这个NSF CAREER项目使用机器学习来推进通过移动的设备提供的健康干预科学。移动的健康（mhealth）领域在全世界广泛使用移动的设备的刺激下正在迅速发展。Mhealth研究人员正在使用移动的设备来诊断、管理和治疗各种健康状况，包括但不限于酗酒、抑郁、肥胖和吸烟。该项目创建了新的学习算法，学习根据个人用户不断变化的需求个性化移动健康干预措施。它还开发了使用用户之间的社交网络进行干预的技术，以便整个网络被引导到积极的健康结果。该项目将为本科研究人员提供机会，研究由社会相关问题驱动的尖端数据科学。它将开发关于顺序决策的新的研究生课程。将公开发布包括特定于健康领域的模拟、学习算法的实现以及提供干预措施的移动的应用程序在内的软件。研究结果将在新成立的密歇根数据科学研究所（MIDAS）的主持下传达给公众，并征求公众的反馈。代表性不足的少数民族的参与将通过密歇根大学的NextProf科学研讨会和多样性计划的教师盟友来鼓励。该项目涉及解决三个技术挑战。首先，现有的顺序决策算法，如上下文强盗算法，需要重新设计，使它们适合在mhealth中采用。该项目将开发行动者-批评者上下文强盗算法，将策略的表示与奖励函数的表示分开。拥有一个可解释的，低维的政策空间是领域科学家学习的政策的可解释性的关键。其次，大多数关于强盗问题和强化学习的文献都假设了平稳性，这在mhealth中是一个不合理的假设。利用无悔学习领域的最新进展，该项目将开发可以处理非平稳性的学习算法。第三，网络干预科学还处于起步阶段。该项目将通过综合复杂网络的高维统计学习和控制的最新进展来设计学习算法，从而在时间演化网络中的少量节点上进行干预，以实现所需的长期目标，从而促进其发展。