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Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients

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Journal of Intensive Medicine 2021年第2期1卷 110-116页

作者： Jonathan Montomoli Luca Romeo Sara Moccia Michele Bernardini Lucia Migliorelli Daniele Berardini Abele Donati Andrea Carsetti Maria Grazia Bocci Pedro David Wendel Garcia Thierry Fumeaux Philippe Guerci reto andreas schüpbach Can Ince Emanuele Frontoni Matthias Peter Hilty RISC-19-ICU Investigators Department of Anaesthesia and Intensive Care Infermi HospitalAUSL della RomagnaRimini 47923Italy Department of Information Engineering UniversitàPolitecnica delle MarcheAncona 60131Italy The BioRobotics Institute and Department of Excellence in Robotics and AI Scuola Superiore Sant’AnnaPisa 56127Italy Anesthesia and Intensive Care Unit Azienda Ospedaliero Universitaria Ospedali RiunitiAncona 60126Italy Department of Biomedical Sciences and Public Health UniversitàPolitecnica delle MarcheAncona 60126Italy Department of Anaesthesia and Intensive Care Fondazione Policlinico Universitario A.Gemelli IRCCSUniversitàCattolica del Sacro CuoreRome 00168Italy Institute of Intensive Care Medicine University Hospital of ZurichZurich 8091Switzerland Swiss Society of Intensive Care Medicine Basel 4001Switzerland Department of Anesthesiology and Critical Care Medicine University Hospital of NancyNancy 54511France Department of Intensive Care Erasmus MC University Medical Center RotterdamRotterdam3015 GDNetherlands 不详

Background:Accurate risk stratification of critically ill patients with coronavirus disease 2019(COVID-19)is essential for optimizing resource allocation,delivering targeted interventions,and maximizing patient survival *** learning(ML)techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical ***:We retrieved data on patients with COVID-19 admitted to an intensive care unit(ICU)between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit(RISC-19-ICU)*** applied the Extreme Gradient Boosting(XGBoost)algorithm to the data to predict as a binary out-come the increase or decrease in patients’Sequential Organ Failure Assessment(SOFA)score on day 5 after ICU *** model was iteratively cross-validated in different subsets of the study ***:The final study population consisted of 675 *** XGBoost model correctly predicted a decrease in SOFA score in 320/385(83%)critically ill COVID-19 patients,and an increase in the score in 210/290(72%)*** area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model(0.86 vs.0.69,P<0.01[paired t-test with 95%confidence interval]).Conclusions:The XGBoost model predicted the change in SOFA score in critically ill COVID-19 patients admitted to the ICU and can guide clinical decision support systems(CDSSs)aimed at optimizing available resources.

关键词： Machine learning Extreme gradient boosting(XGBoost) COVID-19 Multiple organ failure Clinical decision support system(CDSS) Organ dysfunction score

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模糊建筑 2002瑞士世博会,瑞士

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世界建筑导报 2021年第6期36卷 120-121页

作者： Charles Renfro reto Geiser David Huang Karin Ocker andreas Quadenau Deane Simpson Lyn Rice 无不详 DS+R建筑事务所

模糊建筑整体水雾弥漫,是自然与人类的合力之作。纳沙泰尔湖(Lake Neuchatel)湖水经水泵抽出并过滤后,再由3.5万个高压喷嘴喷射成绵绵细雾。智能天气系统随时检测温度、湿度、风速、风向等不断变化的气象条件,调节各个区域的水压压力。... 详细信息

模糊建筑整体水雾弥漫,是自然与人类的合力之作。纳沙泰尔湖(Lake Neuchatel)湖水经水泵抽出并过滤后,再由3.5万个高压喷嘴喷射成绵绵细雾。智能天气系统随时检测温度、湿度、风速、风向等不断变化的气象条件,调节各个区域的水压压力。一踏入"模糊建筑",眼前雾气蔼蔼,耳旁淅淅沥沥,视觉与听觉也会变得模糊起来。沉浸式环境通过越发精湛的科学技术追求高清高保真的视觉体验。而"模糊建筑"逆道而行,极尽朦胧之所能。该展馆设计尝试摆脱周边环境的束缚。游客置身其中,活动无拘无束,目之所及似有似无,宛如梦幻。层层阶梯在水雾中若隐若现,通向蓝天。公众踏着阶梯,可登上天使甲板(Angel Deck)。这座建筑选址湖边。水不仅是其主要材料,也令游客置身其中,宛若步入瑶池,仿佛畅饮琼浆玉液。艺术大师克里斯蒂安·马克雷(Christian Marclay)还采用轻质张拉整体结构,为其内部打造了沉浸式的声学环境。该结构由四根立柱支撑,75英尺高、300英尺宽、200英尺深。

关键词：张拉整体结构高压喷嘴检测温度声学环境世博会置身其中沉浸式建筑选址

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