目的比较CURB一65评分和强化CURB评分对老年重症社区获得性肺炎（severe community acquired pneumonia，SCAP）预后的临床预测价值。方法回顾性分析2009—12—2015-07入住我院急诊科、呼吸内科以及老年呼吸内科的87例老年SCAP相关临床...目的比较CURB一65评分和强化CURB评分对老年重症社区获得性肺炎（severe community acquired pneumonia，SCAP）预后的临床预测价值。方法回顾性分析2009—12—2015-07入住我院急诊科、呼吸内科以及老年呼吸内科的87例老年SCAP相关临床资料，分别统计每例患者的CURB-65评分、强化CURB评分，以患者28d预后为临床观察终点，绘制受试者工作特征（receiver operator characteristic，ROC）曲线，通过比较曲线下面积（area under the curve，AUC）分析两种评分工具对老年SCAP预后的预测价值。结果87例患者CURB一65评分为3（2—3）分，强化CURB评分为11（10～12）分。死亡组中CURB-65评分和强化CURB评分均明显高于存活组，差异有统计学意义（P〈0．05）。强化CURB评分AUC为0．722，最佳截断值为12，敏感度为58．82％，特异度为69．81％，P=0．0001；CURB-65评分AUC为0．660，最佳截断值为3，敏感度为73．53％，特异度为49．06％，P=0．0091。强化CURB评分AUC大于CURB-65评分，差异有统计学意义（0．722vs．0．660，Z=2．176，P=0．029）。结论CURB-65评分和强化CURB评分均可预测老年SCAP预后，强化CURB评分预测价值高于CURB-65评分，且其特异度高于CURB-65评分。展开更多
Curb detection provides road boundary information and is important to road detection.However,curb detection is challenging due to the problems such as various curb shapes,colour,discontinuity.In this work,a novel lear...Curb detection provides road boundary information and is important to road detection.However,curb detection is challenging due to the problems such as various curb shapes,colour,discontinuity.In this work,a novel learning-based method for curb detection is proposed using Lidar point clouds,considering that Lidars are not sensitive to illumination and are relatively stable to weather conditions.A deep neural network,named EdgeNet,is constructed and trained,which handles point clouds in an end-to-end way.After EdgeNet is properly trained,curb points are then segmented in the neural network output.In order to train,a curb point annotation algorithm is also designed to generate training dataset.The curb detection method works well with different road scenarios including intersections.The experimental results validate the effectiveness and robustness of this curb detection method.展开更多
文摘目的比较CURB一65评分和强化CURB评分对老年重症社区获得性肺炎（severe community acquired pneumonia，SCAP）预后的临床预测价值。方法回顾性分析2009—12—2015-07入住我院急诊科、呼吸内科以及老年呼吸内科的87例老年SCAP相关临床资料，分别统计每例患者的CURB-65评分、强化CURB评分，以患者28d预后为临床观察终点，绘制受试者工作特征（receiver operator characteristic，ROC）曲线，通过比较曲线下面积（area under the curve，AUC）分析两种评分工具对老年SCAP预后的预测价值。结果87例患者CURB一65评分为3（2—3）分，强化CURB评分为11（10～12）分。死亡组中CURB-65评分和强化CURB评分均明显高于存活组，差异有统计学意义（P〈0．05）。强化CURB评分AUC为0．722，最佳截断值为12，敏感度为58．82％，特异度为69．81％，P=0．0001；CURB-65评分AUC为0．660，最佳截断值为3，敏感度为73．53％，特异度为49．06％，P=0．0091。强化CURB评分AUC大于CURB-65评分，差异有统计学意义（0．722vs．0．660，Z=2．176，P=0．029）。结论CURB-65评分和强化CURB评分均可预测老年SCAP预后，强化CURB评分预测价值高于CURB-65评分，且其特异度高于CURB-65评分。
基金Supported by the National Natural Science Foundation of China (No.51875331)。
文摘Curb detection provides road boundary information and is important to road detection.However,curb detection is challenging due to the problems such as various curb shapes,colour,discontinuity.In this work,a novel learning-based method for curb detection is proposed using Lidar point clouds,considering that Lidars are not sensitive to illumination and are relatively stable to weather conditions.A deep neural network,named EdgeNet,is constructed and trained,which handles point clouds in an end-to-end way.After EdgeNet is properly trained,curb points are then segmented in the neural network output.In order to train,a curb point annotation algorithm is also designed to generate training dataset.The curb detection method works well with different road scenarios including intersections.The experimental results validate the effectiveness and robustness of this curb detection method.
