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

智能重症监护病房 (I2CU)：普遍传感和人工智能增强临床决策

• 批准号: 10374834
• 负责人: Azra Bihorac
• 金额: $ 59.49万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10374834
• 关键词: Address; Algorithms; Artificial Intelligence; Artificial Intelligence platform; Caring; Clinical; Clinical Data; Clinical Informatics; Clinical assessments; Collaborations; Color; Complex; Computing Methodologies; Critical Care; Critical Illness; Cues; Data; Data Set; Decision Making; Deterioration; Early Diagnosis; Early Intervention; Electronic Health Record; Environment; Evaluation; Facial Expression; Fostering; Foundations; Frequencies; Goals; Health Personnel; Hospital Costs; Human; Image; Intelligence; Intensive Care Units; Intervention; Judgment; Literature; Manuals; Measurement; Medical; Medicine; Mission; Modeling; Monitor; Nurses; Outcome; Pain; Pain Measurement; Patient-Focused Outcomes; Patients; Physical Function; Physicians; Physiological; Posture; Process; Process Assessment; Public Health; Reproducibility; Research; Risk Assessment; Sampling; System; Technology; Testing; Time; Training; United States; United States National Institutes of Health; Visual; advanced disease; augmented intelligence; base; clinical care; clinical decision-making; clinical practice; cost; deep learning; deep learning algorithm; deep learning model; disease diagnosis; emotional distress; improved; indexing; innovation; novel; prediction algorithm; prospective; prospective test; risk perception; satisfaction; sensor; sensor technology; tool; vigilance; Address; Algorithms; Artificial Intelligence; Artificial Intelligence platform; Caring; Clinical; Clinical Data; Clinical Informatics; Clinical assessments; Collaborations; Color; Complex; Computing Methodologies; Critical Care; Critical Illness; Cues; Data; Data Set; Decision Making; Deterioration; Early Diagnosis; Early Intervention; Electronic Health Record; Environment; Evaluation; Facial Expression; Fostering; Foundations; Frequencies; Goals; Health Personnel; Hospital Costs; Human; Image; Intelligence; Intensive Care Units; Intervention; Judgment; Literature; Manuals; Measurement; Medical; Medicine; Mission; Modeling; Monitor; Nurses; Outcome; Pain; Pain Measurement; Patient-Focused Outcomes; Patients; Physical Function; Physicians; Physiological; Posture; Process; Process Assessment; Public Health; Reproducibility; Research; Risk Assessment; Sampling; System; Technology; Testing; Time; Training; United States; United States National Institutes of Health; Visual; advanced disease; augmented intelligence; base; clinical care; clinical decision-making; clinical practice; cost; deep learning; deep learning algorithm; deep learning model; disease diagnosis; emotional distress; improved; indexing; innovation; novel; prediction algorithm; prospective; prospective test; risk perception; satisfaction; sensor; sensor technology; tool; vigilance

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.

项目摘要 尽管对患者敏锐度的密切监测和动态评估是ICU护理的关键方面，但两者都是 受医疗保健提供者施加的时间限制的限制。目前，动态和精确评估 患者在ICU中的敏锐度几乎完全依赖于医生的临床法官和警惕。此外， 捕获重要的视觉评估细节，例如面部表情，姿势和流动性 零星的护士或根本没有被捕获。但是，这些视觉评估细节是 与临界指数（例如身体功能，疼痛和随后的临床定义）相关。 pis’ 长期目标是在自主和 精确的方式。该应用程序的总体目的是开发新颖的工具来传感，量化， 并以自主，精确和可解释的方式传达任何患者的病情。中央 假设是，深度学习模型通过预测敏锐度来优于现有的敏锐度临床评分 使用痛苦，情绪困扰和 身体机能，以及临床和生理数据。该假设已根据 初步数据，并在临床护理文献中得到良好的基础。理由是自主和精确 患者定量可以导致临床工作流程和早期干预。总体目标将 可以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 精确和动态预测患者临床状况的算法，以确定它是否更准确 预测日常护理过渡结果，同时为物理提供可解释的信息。 （2） 开发一个普遍的传感系统，以自主对重症患者的视觉评估以确定 如果它可以与人类专家相比，可以对患者进行准确的视觉评估，并且是否可以丰富敏锐度 预测与临床数据结合使用。 （3）实施和评估一个智能平台 自主视觉评估和敏锐度预测的时间整合，以确定 实时评估的准确性，并确定医生的风险感知和满意度。 方法是创新的，因为它代表了（1）动态预测精确患者的首次尝试 轨迹，（2）自主在ICU中进行视觉评估，（3）实施人工智能 实时在临床工作流程中实时平台。拟议的研究很重要，因为它将解决多个关键 重症监护的问题和关键障碍，包括（1）缺乏精确和实时预测 轨迹，（2）手动重复的ICU评估，以及（3）未受精的患者方面。最终，结果 预计将改善患者预后并降低住院费用，并终身 并发症。