目的探讨静脉性耳鸣患者蛛网膜颗粒内脑疝(brain herniation into arachnoid granulation,BHAG)对于横窦跨狭窄压力梯度(tra ns-stenostic pressure gradient,TPG)升高(≥8m m H g)的诊断效能。方法回顾性分析2010年11月至2023年10月于...目的探讨静脉性耳鸣患者蛛网膜颗粒内脑疝(brain herniation into arachnoid granulation,BHAG)对于横窦跨狭窄压力梯度(tra ns-stenostic pressure gradient,TPG)升高(≥8m m H g)的诊断效能。方法回顾性分析2010年11月至2023年10月于首都医科大学附属北京友谊医院住院的静脉性耳鸣患者临床及影像学资料,所有患者均行静脉窦非增强M RV(non-contrast enhanced MR venography,NCE-MRV)、全脑MPRAGE及3D-T_(2)WI扫描,并经DSA行静脉窦测压,将患者分为A(TPG<8 mm Hg)、B(TPG≥8 mm Hg)两组。结合全脑MPRAGF及3D-T_(2)WI的标准冠状面图像,评价是否存在BHAG。对A、B两组BHAG发生率是否存在差异进行卡方检验;计算BHAG对于诊断高TPG(≥8 mm Hg)特异度、灵敏度、准确度、阳性预测值及阴性预测值。结果共73例患者符合纳排标准(男性9例,女性64例),年龄37.0(46.5-30.0)岁,其中A组(TPG<8 mm Hg)46例,B组(TPG≥8 mm Hg)27例。BHAG患者共12例(12/73,16.44%),A、B两组BHAG发生率存在统计学差异(χ^(2)值=10.96,P<0.001)。BHAG诊断高TPG(≥8mmHg)的特异度为95.65%、灵敏度为37.04%、准确度为73.97%、阳性预测值为83.33%,阴性预测值为72.13%。结论BHAG在静脉性耳鸣患者中并不罕见,可用于辅助预估有无TPG升高。展开更多
With the arrival of the big data era,the phenomenon of information overload is becoming increasingly severe.In response to the common issue of sparse user rating matrices in recommendation systems,a collaborative filt...With the arrival of the big data era,the phenomenon of information overload is becoming increasingly severe.In response to the common issue of sparse user rating matrices in recommendation systems,a collaborative filtering recommendation algorithm was proposed based on improved user profiles in this study.Firstly,a profile labeling system was constructed based on user characteristics.This study proposed that user profile labels should be created using basic user information and basic item information,in order to construct multidimensional user profiles.TF-IDF algorithm was used to determine the weights of user-item feature labels.Secondly,user similarity was calculated by weighting both profile-based collaborative filtering and user-based collaborative filtering algorithms,and the final user similarity was obtained by harmonizing these weights.Finally,personalized recommendations were generated using Top-N method.Validation with the MovieLens-1M dataset revealed that this algorithm enhances both recommendation Precision and Recall compared to single-method approaches(recommendation algorithm based on user portrait and user-based collaborative filtering algorithm).展开更多
In the data-driven era of the internet and business environments,constructing accurate user profiles is paramount for personalized user understanding and classification.The traditional TF-IDF algorithm has some limita...In the data-driven era of the internet and business environments,constructing accurate user profiles is paramount for personalized user understanding and classification.The traditional TF-IDF algorithm has some limitations when evaluating the impact of words on classification results.Consequently,an improved TF-IDF-K algorithm was introduced in this study,which included an equalization factor,aimed at constructing user profiles by processing and analyzing user search records.Through the training and prediction capabilities of a Support Vector Machine(SVM),it enabled the prediction of user demographic attributes.The experimental results demonstrated that the TF-IDF-K algorithm has achieved a significant improvement in classification accuracy and reliability.展开更多
With the rapid development of big data,online education can use big data technology to achieve personalized and intelligent education as well as improve learning effect and user satisfaction.In this study,the users of...With the rapid development of big data,online education can use big data technology to achieve personalized and intelligent education as well as improve learning effect and user satisfaction.In this study,the users of The Open University of China online education platform were taken as the research object,their user behavior data was collected,cleaned,and analyzed with text mining.The RFM model and the improved K-Means algorithm were used to construct the user portrait of the platform group and the needs and preferences of different types of the users were analyzded.Chinese word segmentation was used to show the key words of different types of users and the word cloud of their using frequency.The focus of different user groups was determined to facilitate for the follow-up course recommendation and precision marketing.Experimental results showed that the improved K-Means algorithm can well depict the behavior of group users.The index of silhouette score was improved to 0.811 by the improved K-Means algorithm,from random uncertainty to a fixed value,which can effectively solve the problem of inconsistent results caused by outlier sample points.展开更多
文摘目的探讨静脉性耳鸣患者蛛网膜颗粒内脑疝(brain herniation into arachnoid granulation,BHAG)对于横窦跨狭窄压力梯度(tra ns-stenostic pressure gradient,TPG)升高(≥8m m H g)的诊断效能。方法回顾性分析2010年11月至2023年10月于首都医科大学附属北京友谊医院住院的静脉性耳鸣患者临床及影像学资料,所有患者均行静脉窦非增强M RV(non-contrast enhanced MR venography,NCE-MRV)、全脑MPRAGE及3D-T_(2)WI扫描,并经DSA行静脉窦测压,将患者分为A(TPG<8 mm Hg)、B(TPG≥8 mm Hg)两组。结合全脑MPRAGF及3D-T_(2)WI的标准冠状面图像,评价是否存在BHAG。对A、B两组BHAG发生率是否存在差异进行卡方检验;计算BHAG对于诊断高TPG(≥8 mm Hg)特异度、灵敏度、准确度、阳性预测值及阴性预测值。结果共73例患者符合纳排标准(男性9例,女性64例),年龄37.0(46.5-30.0)岁,其中A组(TPG<8 mm Hg)46例,B组(TPG≥8 mm Hg)27例。BHAG患者共12例(12/73,16.44%),A、B两组BHAG发生率存在统计学差异(χ^(2)值=10.96,P<0.001)。BHAG诊断高TPG(≥8mmHg)的特异度为95.65%、灵敏度为37.04%、准确度为73.97%、阳性预测值为83.33%,阴性预测值为72.13%。结论BHAG在静脉性耳鸣患者中并不罕见,可用于辅助预估有无TPG升高。
文摘With the arrival of the big data era,the phenomenon of information overload is becoming increasingly severe.In response to the common issue of sparse user rating matrices in recommendation systems,a collaborative filtering recommendation algorithm was proposed based on improved user profiles in this study.Firstly,a profile labeling system was constructed based on user characteristics.This study proposed that user profile labels should be created using basic user information and basic item information,in order to construct multidimensional user profiles.TF-IDF algorithm was used to determine the weights of user-item feature labels.Secondly,user similarity was calculated by weighting both profile-based collaborative filtering and user-based collaborative filtering algorithms,and the final user similarity was obtained by harmonizing these weights.Finally,personalized recommendations were generated using Top-N method.Validation with the MovieLens-1M dataset revealed that this algorithm enhances both recommendation Precision and Recall compared to single-method approaches(recommendation algorithm based on user portrait and user-based collaborative filtering algorithm).
文摘In the data-driven era of the internet and business environments,constructing accurate user profiles is paramount for personalized user understanding and classification.The traditional TF-IDF algorithm has some limitations when evaluating the impact of words on classification results.Consequently,an improved TF-IDF-K algorithm was introduced in this study,which included an equalization factor,aimed at constructing user profiles by processing and analyzing user search records.Through the training and prediction capabilities of a Support Vector Machine(SVM),it enabled the prediction of user demographic attributes.The experimental results demonstrated that the TF-IDF-K algorithm has achieved a significant improvement in classification accuracy and reliability.
文摘With the rapid development of big data,online education can use big data technology to achieve personalized and intelligent education as well as improve learning effect and user satisfaction.In this study,the users of The Open University of China online education platform were taken as the research object,their user behavior data was collected,cleaned,and analyzed with text mining.The RFM model and the improved K-Means algorithm were used to construct the user portrait of the platform group and the needs and preferences of different types of the users were analyzded.Chinese word segmentation was used to show the key words of different types of users and the word cloud of their using frequency.The focus of different user groups was determined to facilitate for the follow-up course recommendation and precision marketing.Experimental results showed that the improved K-Means algorithm can well depict the behavior of group users.The index of silhouette score was improved to 0.811 by the improved K-Means algorithm,from random uncertainty to a fixed value,which can effectively solve the problem of inconsistent results caused by outlier sample points.
