Background."Critical value reporting has been widely adopted by hospitals throughout the world,but there were few reports about neonatal critical values.This study aimed to analyze characteristics of the neonatal...Background."Critical value reporting has been widely adopted by hospitals throughout the world,but there were few reports about neonatal critical values.This study aimed to analyze characteristics of the neonatal critical values considered at our center and to provide information on improving neonatal intensive care.Methods:A retrospective study of critical values at a newborn tertiary center in China was conducted to assess neonatal critical values according to test,distribution,reporting time,patient outcome and the impact to the therapy.展开更多
目的分析实验室检验质量管理工作中应用实验室信息管理系统(Laboratory Information Management System,LIMS)对检验回报合格率存在的影响。方法从本实验室于2022年1月-2023年12月进行检验的标本中选取328份,根据时间分为常规组(2022年1...目的分析实验室检验质量管理工作中应用实验室信息管理系统(Laboratory Information Management System,LIMS)对检验回报合格率存在的影响。方法从本实验室于2022年1月-2023年12月进行检验的标本中选取328份,根据时间分为常规组(2022年1月-2022年12月,常规检验质量管理)、观察组(2023年1月-2023年12月,通过LIMS系统完成检验质量管理),各164份。比较两组标本检验报告情况、不良事件发生情况及实验室工作人员满意度。结果对比两组标本检验报告情况,观察组危急值回报率(95.73%)、危急值报告及时率(96.95%)、检验回报合格率(97.56%)均更高(χ^(2)=6.821,4.525,5.878,P<0.05);组间不良事件发生率对比,观察组(3.05%)更低(χ^(2)=4.525,P<0.05);相较于LIMS系统实施前,实施后实验室工作人员工作效率[(9.12±0.55)分]、管理效果[(9.16±0.57)分]、风险把控[(9.27±0.54)分]评分均更高(t=2.369,2.270,2.291,P<0.05)。结论在LIMS系统下开展实验室检验质量管理工作,可提高检验回报合格率、危急值回报率及危急值回报及时率,降低不良事件发生风险,实验室工作人员管理满意度随之提高,实践价值显著。展开更多
We describe here a comprehensive framework for intelligent information management (IIM) of data collection and decision-making actions for reliable and robust event processing and recognition. This is driven by algori...We describe here a comprehensive framework for intelligent information management (IIM) of data collection and decision-making actions for reliable and robust event processing and recognition. This is driven by algorithmic information theory (AIT), in general, and algorithmic randomness and Kolmogorov complexity (KC), in particular. The processing and recognition tasks addressed include data discrimination and multilayer open set data categorization, change detection, data aggregation, clustering and data segmentation, data selection and link analysis, data cleaning and data revision, and prediction and identification of critical states. The unifying theme throughout the paper is that of “compression entails comprehension”, which is realized using the interrelated concepts of randomness vs. regularity and Kolmogorov complexity. The constructive and all encompassing active learning (AL) methodology, which mediates and supports the above theme, is context-driven and takes advantage of statistical learning, in general, and semi-supervised learning and transduction, in particular. Active learning employs explore and exploit actions characteristic of closed-loop control for evidence accumulation in order to revise its prediction models and to reduce uncertainty. The set-based similarity scores, driven by algorithmic randomness and Kolmogorov complexity, employ strangeness / typicality and p-values. We propose the application of the IIM framework to critical states prediction for complex physical systems;in particular, the prediction of cyclone genesis and intensification.展开更多
基金National Natural Science Foundation of China(No.81370744,81571483,81601323).Doctoral Degree Funding from Chinese Ministry of Education(No.20135503110009).State key clinic discipline project(No.2011-873).the Scientific Research Foundation of The science and Technology Commission of Chongqing(No.cstc2015jcyjA10089).Clinical Research Foundation of Children's Hospital of Chongqing Medical University(254lcyj2014-11).
文摘Background."Critical value reporting has been widely adopted by hospitals throughout the world,but there were few reports about neonatal critical values.This study aimed to analyze characteristics of the neonatal critical values considered at our center and to provide information on improving neonatal intensive care.Methods:A retrospective study of critical values at a newborn tertiary center in China was conducted to assess neonatal critical values according to test,distribution,reporting time,patient outcome and the impact to the therapy.
文摘目的分析实验室检验质量管理工作中应用实验室信息管理系统(Laboratory Information Management System,LIMS)对检验回报合格率存在的影响。方法从本实验室于2022年1月-2023年12月进行检验的标本中选取328份,根据时间分为常规组(2022年1月-2022年12月,常规检验质量管理)、观察组(2023年1月-2023年12月,通过LIMS系统完成检验质量管理),各164份。比较两组标本检验报告情况、不良事件发生情况及实验室工作人员满意度。结果对比两组标本检验报告情况,观察组危急值回报率(95.73%)、危急值报告及时率(96.95%)、检验回报合格率(97.56%)均更高(χ^(2)=6.821,4.525,5.878,P<0.05);组间不良事件发生率对比,观察组(3.05%)更低(χ^(2)=4.525,P<0.05);相较于LIMS系统实施前,实施后实验室工作人员工作效率[(9.12±0.55)分]、管理效果[(9.16±0.57)分]、风险把控[(9.27±0.54)分]评分均更高(t=2.369,2.270,2.291,P<0.05)。结论在LIMS系统下开展实验室检验质量管理工作,可提高检验回报合格率、危急值回报率及危急值回报及时率,降低不良事件发生风险,实验室工作人员管理满意度随之提高,实践价值显著。
文摘We describe here a comprehensive framework for intelligent information management (IIM) of data collection and decision-making actions for reliable and robust event processing and recognition. This is driven by algorithmic information theory (AIT), in general, and algorithmic randomness and Kolmogorov complexity (KC), in particular. The processing and recognition tasks addressed include data discrimination and multilayer open set data categorization, change detection, data aggregation, clustering and data segmentation, data selection and link analysis, data cleaning and data revision, and prediction and identification of critical states. The unifying theme throughout the paper is that of “compression entails comprehension”, which is realized using the interrelated concepts of randomness vs. regularity and Kolmogorov complexity. The constructive and all encompassing active learning (AL) methodology, which mediates and supports the above theme, is context-driven and takes advantage of statistical learning, in general, and semi-supervised learning and transduction, in particular. Active learning employs explore and exploit actions characteristic of closed-loop control for evidence accumulation in order to revise its prediction models and to reduce uncertainty. The set-based similarity scores, driven by algorithmic randomness and Kolmogorov complexity, employ strangeness / typicality and p-values. We propose the application of the IIM framework to critical states prediction for complex physical systems;in particular, the prediction of cyclone genesis and intensification.
