CAREER: Fundamental Intelligent Building Blocks of the Intensive Care Unit (ICU) of the Future

在美国，重症监护病房（ICU）费用超过全国卫生费用的4%，ICU死亡率可高达29%。对ICU患者状态的精确评估和预测可以使早期干预成为可能，并可以改善患者的预后。然而，今天的icu在评估和预测患者状态方面仍然面临许多障碍。首先，诸如疼痛和功能状态等基本信息不是自动捕获的，而是由负担过重的ICU护士重复测量，每年增加新的评估。其次，现有的预测患者状态的方法准确性有限，而且使用频率不高（例如，每24小时一次，而不是实时使用）。这导致错失早期干预的机会。最后，现有的模型不能自动纳入家庭照顾者的反馈，以改善患者的状态预测。总之，这些挑战表明，迫切需要开发未来icu的几个基本智能构建模块。这些构建模块应该解决：(a)如何学习新的患者状态预测模型而不影响先前预测模型的性能，(b)如何处理复杂的临床数据以精确预测患者结果，以及(c)如何将家庭照顾者的反馈纳入预测模型。该项目将追求三个具体的研究目标，以解决这些问题：(1)终身多任务学习：将开发新的多任务深度学习模型，用于识别与疼痛和功能状态评估相关的临床表现和功能活动。这些模型将以创新的方式定制，以最大限度地实现相关任务之间的信息共享。(2)多尺度和动态学习：新的多尺度递归神经网络将被开发出来，在处理多个时间尺度和隐式输入随时间变化的同时，预测精确的患者结果。(3)持续机会学习：将开发新的主动深度学习模型来查询最具信息量的数据点的标签，以便随着时间的推移改进模型，同时对用户的负担最小。拟议的项目将把机器学习算法和重症监护医学的新元素结合在一起。这将是首次尝试在ICU中自主评估疼痛和功能状态，从高分辨率数据中预测精确的患者轨迹，并通过用户反馈改进预测临床模型。此外，这项研究将具有影响力，因为在这里学到的东西将有助于更广泛地理解下一代终身学习系统和智能医院的未来设计考虑因素。PI还将在智能ICU的背景下为佛罗里达高中教师和学生以及佛罗里达大学（UF）本科生提供高度集成的研究和教育计划。该计划建议：(1)资助数学教师暑期实习；(2)为高中生组织一个关于编码和机器智能的智能机器研讨会；(3)为本科生开展重点研究和培训活动。这些外展和培训计划将用于促进在科学，技术，工程和数学（STEM）领域的佛罗里达高中学生和UF本科生的兴趣。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。

In the United States, intensive care units (ICUs) costs exceed 4% of national health costs, and ICU mortality rates can be as high as 29%. Precise assessment and prediction of patient status in the ICU can enable early interventions, and can result in improved patient outcomes. However, today's ICUs still face many barriers for assessing and predicting patient status. First, essential information such as pain and functional status are not captured automatically, but rather are repetitively measured by overburdened ICU nurses, with new assessments added each year. Second, existing methods for predicting patient status have limited accuracy and are used infrequently (e.g. every 24 hours as opposed to in real-time). This leads to missing opportunities for early interventions. Finally, existing models cannot automatically incorporate family caregiver feedback for improved patient status prediction. Together, these challenges point to the critical need for developing several fundamental intelligent building blocks of future ICUs. These building blocks should address: (a) how to learn new patient status prediction models without compromising performance on previous prediction models, (b) how to handle the complex clinical data for precise prediction of patient outcome, and (c) how to incorporate family caregiver feedback into the prediction models.This project will pursue three specific research objectives that will address these issues: (1) Lifelong Multi-Task Learning: Novel multi-task deep learning models will be developed for recognizing clinical expressions and functional activities related to pain and functional status assessments. These models will be customized in an innovative manner to maximize information sharing among related tasks. (2) Multi-Scale and Dynamic Learning: Novel multi-scale recurrent neural networks will be developed to predict precise patient outcomes while handling multiple temporal scales and implicit input changes over time. (3) Continual Opportunistic Learning: Novel active deep learning models will be developed to query the labels of the most informative data points for improving the models over time, with minimum burden on users. The proposed project will bring together novel elements of machine learning algorithms and critical care medicine. This will represent the first attempt to autonomously assess pain and functional status in the ICU, to predict precise patient trajectory from high-resolution data, and to improve predictive clinical models through user feedback. In addition, the research will be impactful because what is learned here, will contribute to a broader understanding of future design considerations for the next generation of lifelong learning systems and intelligent hospitals. The PI will also provide a highly-integrated research and educational program for Florida high school teachers and students, and University of Florida (UF) undergraduate students in the context of the intelligent ICU. The PI proposes to: (1) sponsor summer internships for math teachers, (2) organize an Intelligent Machines workshop on coding and machine intelligence for the high school students, and (3) develop focused research and training activities for undergraduate students. These outreach and training programs will be used to promote interest in science, technology, engineering, and mathematics (STEM) fields among Florida high school students and UF undergraduate students.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.