AI增强型重症监护病房：利用普适感知革新患者护理
AI-Enhanced Intensive Care Unit: Revolutionizing Patient Care with Pervasive Sensing
AI总结：
本文提出智能重症监护病房（I2CU）系统，通过融合深度图像、RGB、加速度、肌电等多模态普适感知数据，实现连续细粒度的患者病情、谵妄、疼痛和活动能力评估，以弥补ICU人工视觉监测的不足。
摘要：
重症监护病房（ICU）是医院中的特殊区域，危重患者在此接受重症监护和监测。全面监测对于评估患者状况（尤其是病情严重程度）以及最终评估护理质量至关重要。然而，由于时间限制和医护人员的繁重工作量，ICU中患者监测的覆盖范围受到限制。目前，针对病情严重程度的视觉评估，包括面部表情、姿势和活动能力等细节，要么只是零星地被记录，要么完全没有被记录。这些人工观察因观察者个体而异，容易出现记录错误，并给护理人员带来额外的工作负担。人工智能（AI）驱动的系统因其卓越的学习能力，有潜力增强对患者的视觉监测和评估。此类系统需要稳健的标注数据来进行训练。为此，我们开发了普适感知和数据处理系统，该系统在ICU中采集来自多种模态的数据，包括深度图像、彩色RGB图像、加速度测量、肌电图、声压和光照水平，用于开发智能监测系统，以实现连续且细粒度的病情严重程度、谵妄风险、疼痛和活动能力评估。本文介绍了我们为实时患者监测和视觉评估所开发的智能重症监护病房（I2CU）系统架构。
英文摘要：
The intensive care unit (ICU) is a specialized hospital space where critically ill patients receive intensive care and monitoring. Comprehensive monitoring is imperative in assessing patients conditions, in particular acuity, and ultimately the quality of care. However, the extent of patient monitoring in the ICU is limited due to time constraints and the workload on healthcare providers. Currently, visual assessments for acuity, including fine details such as facial expressions, posture, and mobility, are sporadically captured, or not captured at all. These manual observations are subjective to the individual, prone to documentation errors, and overburden care providers with the additional workload. Artificial Intelligence (AI) enabled systems has the potential to augment the patient visual monitoring and assessment due to their exceptional learning capabilities. Such systems require robust annotated data to train. To this end, we have developed pervasive sensing and data processing system which collects data from multiple modalities depth images, color RGB images, accelerometry, electromyography, sound pressure, and light levels in ICU for developing intelligent monitoring systems for continuous and granular acuity, delirium risk, pain, and mobility assessment. This paper presents the Intelligent Intensive Care Unit (I2CU) system architecture we developed for real-time patient monitoring and visual assessment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
