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的敏锐度是ICU护理的关键方面，但两者都是 受限于对医疗保健提供者施加的时间限制。目前，动态和准确地评估 ICU患者的敏锐度几乎完全依赖于医生的临床判断和警觉。此外， 捕捉重要的视觉评估细节，如面部表情、姿势和移动性 偶尔被负担过重的护士捕获或根本不被捕获。然而，这些视觉评估细节是 与身体功能、疼痛和随后的临床恶化等关键指标相关。私人侦探的 长期目标是在一种自主的和 严谨的态度。该应用程序的总体目标是开发用于检测、量化、 并以自主、准确和可解释的方式传达任何患者的情况。中环 假设深度学习模型将通过预测敏感度来优于现有的敏感度临床评分 一种动态、准确和可解释的方式，使用对痛苦、情绪困扰和 身体功能，以及临床和生理数据。这一假设是基于以下假设提出的 初步数据，并在临床护理文献中有良好的基础。其基本原理是自主和精确 患者量化可以改善临床工作流程和早期干预。总的目标是 通过追求三个具体目标来实现。(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临床状态的算法，以确定是否在 预测日常护理过渡结果，同时向医生提供可解释的信息。(2) 开发用于危重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，它能为患者提供准确的视觉评估，以及它是否能增强敏锐度 结合临床数据进行预测。(3)实施和评估智能平台，以实现- 自主视觉评估和视力预测在临床工作流程中的时间整合确定 实时前瞻性评估的准确性，并确定医生的风险认知和满意度。这个 方法是创新的，因为它代表了(1)动态预测精确患者的第一次尝试 轨迹，(2)在ICU自主进行视觉评估，(3)实现人工智能 临床工作流程中的实时平台。拟议的研究具有重要意义，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)缺乏对临床的准确和实时预测 轨迹，(2)人工重复的ICU评估，以及(3)未捕获的患者方面。最终，结果是 预计将改善患者的预后，降低住院成本，以及终身 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.