Intelligent Intensive Care Unit (I2CU): Pervasive Sensing and Artificial Intelligence for Augmented Clinical Decision-making

项目摘要 尽管密切监测和动态评估患者病情是ICU护理的关键方面， 受强加于医疗保健提供者的时间限制的限制。目前，动态和精确的评估 ICU中患者的病情紧急程度几乎完全取决于医生的临床判断和警惕性。此外，委员会认为， 重要的视觉评估细节，如面部表情、姿势和移动性，都被捕获 偶尔由负担过重的护士或根本没有被捕获。然而，这些视觉评估细节 与身体功能、疼痛和随后的临床恶化等关键指标相关。私家侦探 长期目标是在一个自主的， 精确的方式。本申请的总体目标是开发用于感测、量化 并以自主、精确和可解释的方式传达任何患者的状况。中央 假设是，深度学习模型将通过预测以下方面的敏锐度而上级于现有敏锐度临床评分： 一个动态的，精确的，可解释的方式，使用自主评估的疼痛，情绪困扰， 身体功能，以及临床和生理数据。该假设是基于以下几点提出的： 初步数据，并在临床护理文献中有充分的依据。其原理是，自主和精确 患者量化可以导致增强的临床工作流程和早期干预。总体目标将 要实现这三个具体目标。(1)开发和验证可解释的深度学习 用于精确和动态预测患者的临床状态的算法，以确定其是否更准确， 预测日常护理过渡结果，同时为医生提供可解释的信息。（二） 开发一种用于重症患者自主视觉评估的普适传感系统，以确定 如果与人类专家相比，其能够提供患者准确视觉评估，且如果其能够丰富敏锐度 结合临床数据进行预测。(3)实施和评估真实的智能平台- 在临床工作流程中对自主视力评估和敏锐度预测进行时间整合， 实时前瞻性评估的准确性，并确定医生的风险感知和满意度。的 这种方法是创新的，因为它代表了第一次尝试（1）动态预测精确的患者 轨迹，（2）在ICU中自主执行视觉评估，以及（3）实施人工智能 在临床工作流程中真实的实时使用平台。这项研究是重要的，因为它将解决几个关键问题。 重症监护中的问题和关键障碍，包括（1）缺乏精确和实时的临床预测 轨迹，（2）手动重复ICU评估，以及（3）未捕获的患者方面。最终，结果 预计将改善患者的预后，降低住院费用， 并发症

Project Summary Although close monitoring and dynamic assessment of patient acuity are key aspects of ICU care, both are limited by the time constraints imposed on healthcare providers. Currently, dynamic and precise assessment of patient’s acuity in ICU rely almost exclusively on physicians’ clinical judgment and vigilance. Furthermore, important visual assessment details, such as facial expressions, posture, and mobility, are captured sporadically by overburdened nurses or are not captured at all. However, these visual assessment details are associated with critical indices such as physical function, pain and subsequent clinical deterioration. The PIs’ long-term goal is to sense, quantify, and communicate patient’s clinical condition in an autonomous and precise manner. The overall objective of this application is to develop the novel tools for sensing, quantifying, and communicating any patient’s condition in an autonomous, precise, and interpretable manner. The central hypothesis is that deep learning models will be superior to existing acuity clinical scores by predicting acuity in a dynamic, precise, and interpretable manner, using autonomous assessment of pain, emotional distress and physical function, together with clinical and physiologic data. The hypothesis has been formulated based on preliminary data and is well-grounded in clinical care literature. The rationale is that autonomous and precise patient quantification can result in enhanced clinical workflow and early intervention. The overall objective will be achieved by pursuing three specific aims. (1) Developing and validating an interpretable deep learning algorithm for precise and dynamic prediction of the patient’s clinical status to determine if it is more accurate in predicting daily care transition outcomes, while providing interpretable information to the physician. (2) Developing a pervasive sensing system for autonomous visual assessment of critically ill patients to determine if it can provide accurate visual assessment of a patient compared to human expert, and if it can enrich acuity prediction when combined with clinical data. (3) Implementing and evaluating an intelligent platform for real- time integration of autonomous visual assessment and acuity prediction in clinical workflow to determine accuracy in real-time prospective evaluation and to determine physicians’ risk perception and satisfaction. The approach is innovative, because it represents the first attempt to (1) dynamically predict precise patient trajectory, (2) autonomously perform visual assessment in the ICU, and (3) implement artificial intelligence platform in real time in clinical workflow. The proposed research is significant since it will address several key problems and critical barriers in critical care, including (1) lack of precise and real-time prediction of clinical trajectory, (2) manual repetitive ICU assessments, and (3) uncaptured patient aspects. Ultimately, the results are expected to improve patient outcomes and decrease hospitalization costs, as well as lifelong complications.