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

基本信息

批准号：
10154047
负责人：
Azra Bihorac
金额：
$ 63.21万
依托单位：
UNIVERSITY OF FLORIDA
依托单位国家：
美国
项目类别：
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/10154047
关键词：
Address Algorithms Artificial Intelligence 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 disease diagnosis emotional distress improved indexing innovation novel prediction algorithm prospective prospective test risk perception satisfaction sensor sensor technology tool vigilance

项目摘要

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.

项目概要尽管密切监测和动态评估患者病情严重程度是 ICU 护理的关键方面，但两者都目前，由于医疗保健提供者的时间限制，动态和精确的评估。 ICU 患者的病情严重程度几乎取决于医生的临床判断和警惕性。捕捉重要的视觉评估细节，例如面部表情、姿势和活动能力然而，这些视觉评估细节是由负担过重的护士偶尔捕捉到的，或者根本没有捕捉到。与身体功能、疼痛和随后的临床恶化等关键指标相关。长期目标是以自主且可靠的方式感知、量化和传达患者的临床状况该应用程序的总体目标是开发用于传感、量化、以自主、精确和可解释的方式传达任何患者的病情。假设深度学习模型通过预测患者的敏锐度将优于现有的敏锐度临床评分采用动态、精确和可解释的方式，使用对疼痛、情绪困扰和该假设是根据身体机能以及临床和生理数据制定的。初步数据并有充分的临床护理文献基础，其基本原理是自主且精确。患者量化可以增强临床工作流程和早期干预。 (1) 开发和验证可解释的深度学习精确动态预测患者临床状态的算法，以确定其是否更准确预测日常护理过渡结果，同时向医生提供可解释的信息 (2)。开发一种普遍传感系统，用于对重症患者进行自主视觉评估，以确定与人类专家相比，它是否可以提供对患者的准确视觉评估，以及是否可以提高敏锐度 (3) 实施和评估实时智能平台。临床工作流程中自主视觉评估和准确性预测的时间整合，以确定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。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) 在 ICU 中自主执行视觉评估，(3) 实施人工智能所提出的研究具有重要意义，因为它将解决几个关键问题。重症监护中存在的问题和关键障碍，包括（1）缺乏准确、实时的临床预测 (2) 手动重复 ICU 评估，以及 (3) 未捕获的患者方面最终的结果。预计将改善患者的治疗效果并降低住院费用以及终身受益并发症。