脓毒症是一种高发病率、病死率的急危重综合征,早期诊断与治疗是改善其预后的关键。尽管现代医疗技术有了长足的进步,但脓毒症的及时而精准的诊断仍面临严峻挑战。近年来人工智能发展迅速,在医疗方面的应用日益广泛,在临床多种疾病的诊...脓毒症是一种高发病率、病死率的急危重综合征,早期诊断与治疗是改善其预后的关键。尽管现代医疗技术有了长足的进步,但脓毒症的及时而精准的诊断仍面临严峻挑战。近年来人工智能发展迅速,在医疗方面的应用日益广泛,在临床多种疾病的诊疗中成果颇丰。深度学习属于人工智能的前沿技术,可分析海量、高维的医疗数据,为脓毒症的诊断提供了一个新思路。本文总结了深度学习在脓毒症诊断中的研究进展,以期为脓毒症的诊疗提供参考。Sepsis is an acute and critical syndrome with high incidence rate and mortality. Early diagnosis and treatment is the key to improving its prognosis. Despite significant advances in modern medical technology, timely and accurate diagnosis of sepsis still faces serious challenges. In recent years, artificial intelligence has developed rapidly and its applications in healthcare have become increasingly widespread, with fruitful results in the diagnosis and treatment of various diseases in clinical practice. Deep learning is a cutting-edge technology in artificial intelligence that can analyze massive and high-dimensional medical data, providing a new approach for the diagnosis of sepsis. This article summarizes the research progress of deep learning in the diagnosis of sepsis, in order to provide reference for the diagnosis and treatment of sepsis.展开更多
目的了解ICU护士俯卧位通气(prone position ventilation,PPV)临床实施现况及障碍因素。方法采用便利抽样法,选取2023年3-4月山东省济南、青岛、济宁等5个地级市三甲医院急诊ICU、内科ICU、外科ICU、综合ICU等科室的ICU护士230名,采用...目的了解ICU护士俯卧位通气(prone position ventilation,PPV)临床实施现况及障碍因素。方法采用便利抽样法,选取2023年3-4月山东省济南、青岛、济宁等5个地级市三甲医院急诊ICU、内科ICU、外科ICU、综合ICU等科室的ICU护士230名,采用自行设计的一般资料调查问卷和ICU护士PPV临床实施现状及障碍因素调查问卷进行调查。结果约>90%的护士落实执行了PPV流程规范的基本要求、评估要点、实施与护理要点,但ICU护士对患者实施俯卧位前进行镇痛镇静评估的执行率(85.4%)低于俯卧位期间镇痛镇静评估的执行率(91.3%)。PPV相关并发症中,压力性损伤(84.9%)、颜面部水肿(79.9%)、胃内容物反流与误吸(70.3%)发生率排名前3。ICU护士PPV临床实施障碍因素各维度得分由高到低依次是“证据相关因素”“目标人群-患者或家属相关因素”“资源相关因素”“目标人群-医护人员相关因素”“目标人群-团队相关因素”“组织相关因素”。得分最高的条目是“证据相关因素”维度中的“相关证据适用范围有限,不是所有的患者都适合开展”。结论ICU护士PPV临床实践态度较为积极,实施过程有待继续优化,应重点关注其实施过程的薄弱环节,并制定针对性措施,进一步完善PPV护理流程。展开更多
文摘脓毒症是一种高发病率、病死率的急危重综合征,早期诊断与治疗是改善其预后的关键。尽管现代医疗技术有了长足的进步,但脓毒症的及时而精准的诊断仍面临严峻挑战。近年来人工智能发展迅速,在医疗方面的应用日益广泛,在临床多种疾病的诊疗中成果颇丰。深度学习属于人工智能的前沿技术,可分析海量、高维的医疗数据,为脓毒症的诊断提供了一个新思路。本文总结了深度学习在脓毒症诊断中的研究进展,以期为脓毒症的诊疗提供参考。Sepsis is an acute and critical syndrome with high incidence rate and mortality. Early diagnosis and treatment is the key to improving its prognosis. Despite significant advances in modern medical technology, timely and accurate diagnosis of sepsis still faces serious challenges. In recent years, artificial intelligence has developed rapidly and its applications in healthcare have become increasingly widespread, with fruitful results in the diagnosis and treatment of various diseases in clinical practice. Deep learning is a cutting-edge technology in artificial intelligence that can analyze massive and high-dimensional medical data, providing a new approach for the diagnosis of sepsis. This article summarizes the research progress of deep learning in the diagnosis of sepsis, in order to provide reference for the diagnosis and treatment of sepsis.
文摘目的了解ICU护士俯卧位通气(prone position ventilation,PPV)临床实施现况及障碍因素。方法采用便利抽样法,选取2023年3-4月山东省济南、青岛、济宁等5个地级市三甲医院急诊ICU、内科ICU、外科ICU、综合ICU等科室的ICU护士230名,采用自行设计的一般资料调查问卷和ICU护士PPV临床实施现状及障碍因素调查问卷进行调查。结果约>90%的护士落实执行了PPV流程规范的基本要求、评估要点、实施与护理要点,但ICU护士对患者实施俯卧位前进行镇痛镇静评估的执行率(85.4%)低于俯卧位期间镇痛镇静评估的执行率(91.3%)。PPV相关并发症中,压力性损伤(84.9%)、颜面部水肿(79.9%)、胃内容物反流与误吸(70.3%)发生率排名前3。ICU护士PPV临床实施障碍因素各维度得分由高到低依次是“证据相关因素”“目标人群-患者或家属相关因素”“资源相关因素”“目标人群-医护人员相关因素”“目标人群-团队相关因素”“组织相关因素”。得分最高的条目是“证据相关因素”维度中的“相关证据适用范围有限,不是所有的患者都适合开展”。结论ICU护士PPV临床实践态度较为积极,实施过程有待继续优化,应重点关注其实施过程的薄弱环节,并制定针对性措施,进一步完善PPV护理流程。
