Gestational Diabetes Mellitus (GDM) is a significant health concern affecting pregnant women worldwide. It is characterized by elevated blood sugar levels during pregnancy and poses risks to both maternal and fetal he...Gestational Diabetes Mellitus (GDM) is a significant health concern affecting pregnant women worldwide. It is characterized by elevated blood sugar levels during pregnancy and poses risks to both maternal and fetal health. Maternal complications of GDM include an increased risk of developing type 2 diabetes later in life, as well as hypertension and preeclampsia during pregnancy. Fetal complications may include macrosomia (large birth weight), birth injuries, and an increased risk of developing metabolic disorders later in life. Understanding the demographics, risk factors, and biomarkers associated with GDM is crucial for effective management and prevention strategies. This research aims to address these aspects comprehensively through the analysis of a dataset comprising 600 pregnant women. By exploring the demographics of the dataset and employing data modeling techniques, the study seeks to identify key risk factors associated with GDM. Moreover, by analyzing various biomarkers, the research aims to gain insights into the physiological mechanisms underlying GDM and its implications for maternal and fetal health. The significance of this research lies in its potential to inform clinical practice and public health policies related to GDM. By identifying demographic patterns and risk factors, healthcare providers can better tailor screening and intervention strategies for pregnant women at risk of GDM. Additionally, insights into biomarkers associated with GDM may contribute to the development of novel diagnostic tools and therapeutic approaches. Ultimately, by enhancing our understanding of GDM, this research aims to improve maternal and fetal outcomes and reduce the burden of this condition on healthcare systems and society. However, it’s important to acknowledge the limitations of the dataset used in this study. Further research utilizing larger and more diverse datasets, perhaps employing advanced data analysis techniques such as Power BI, is warranted to corroborate and expand upon the findings of this research. This underscores the ongoing need for continued investigation into GDM to refine our understanding and improve clinical management strategies.展开更多
目的利用Power BI Desktop对医院DIP分组明细进行多维度对比分析及数据可视化,寻找医院DIP亏损原因。方法提取某三甲肿瘤专科医院2022年度部分出院结算DIP入组明细数据,通过数据导入、数据类型转换、建立数据关系、数据建模、数据可视...目的利用Power BI Desktop对医院DIP分组明细进行多维度对比分析及数据可视化,寻找医院DIP亏损原因。方法提取某三甲肿瘤专科医院2022年度部分出院结算DIP入组明细数据,通过数据导入、数据类型转换、建立数据关系、数据建模、数据可视化等方法,建立全院、科室、病种等多个维度的可视化动态分析报表。结果Power BI Desktop能有效提升DIP精细化管理水平,提升工作效率,精准分析亏损原因,指导科室进行改进,有效减少医院DIP医保资金亏损。结论Power BI Desktop具有成本低廉,定制化、可视化、自动化程度高,用户界面友好等特点,值得在各医院尤其是信息化程度低、专科运营人员不足、资金预算不足的医院DIP精细化管理中进行推广。展开更多
This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of co...This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of computer science in different fields of study. The technique used in this paper is handling the inadequate Information for citation;it removes the problem of cold start, which is encountered by very many other recommender systems. In this paper, abstracts, the titles, and the Microsoft academic graphs have been used in coming up with the recommendation list for every document, which is used to combine the content-based approaches and the co-citations. Prioritization and the blending of every technique have been allowed by the tuning system parameters, allowing for the authority in results of recommendation versus the paper novelty. In the end, we do observe that there is a direct correlation between the similarity rankings that have been produced by the system and the scores of the participant. The results coming from the associated scrips of analysis and the user survey have been made available through the recommendation system. Managers must gain the required expertise to fully utilize the benefits that come with business intelligence systems [1]. Data mining has become an important tool for managers that provides insights about their daily operations and leverage the information provided by decision support systems to improve customer relationships [2]. Additionally, managers require business intelligence systems that can rank the output in the order of priority. Ranking algorithm can replace the traditional data mining algorithms that will be discussed in-depth in the literature review [3].展开更多
为强化心电信号渐变特征、提高时序信号分类精度,基于Bi-LSTM提出了一种融合渐变数据特征的自适应算法IGF+Bi-LSTM(self-adaptive Bi-LSTM based on integrating gradient features)。该算法在一定范围内自适应选取相似程度最高的渐变...为强化心电信号渐变特征、提高时序信号分类精度,基于Bi-LSTM提出了一种融合渐变数据特征的自适应算法IGF+Bi-LSTM(self-adaptive Bi-LSTM based on integrating gradient features)。该算法在一定范围内自适应选取相似程度最高的渐变数据特征,通过数据融合强化渐变特征在网络隐空间的交互,拓展Bi-LSTM信息传递模式;针对时序信号间存在周期性不匹配和强度不一致的问题,提出一种基于差分的改进的B式距离,以刻画数据和不同标签数据全体间的差异度S并自适应调整IGF+Bi-LSTM中的融合系数。实验表明,该算法在ECG数据集上的分类精度达到98.7%,F1值为98.7%,证明了IGF+Bi-LSTM算法的有效性和实用性。展开更多
该文分析高校学生基础信息数据特点,针对本地数据管理不规范、不可追溯、关联性不足等问题,分别对Tableau Prep Builder和Power BI 2种商业BI工具数据预处理功能进行研究分析,提出基于2种BI工具的数据预处理的实现路径,并对2种数据处理...该文分析高校学生基础信息数据特点,针对本地数据管理不规范、不可追溯、关联性不足等问题,分别对Tableau Prep Builder和Power BI 2种商业BI工具数据预处理功能进行研究分析,提出基于2种BI工具的数据预处理的实现路径,并对2种数据处理方式进行比较。展开更多
文摘Gestational Diabetes Mellitus (GDM) is a significant health concern affecting pregnant women worldwide. It is characterized by elevated blood sugar levels during pregnancy and poses risks to both maternal and fetal health. Maternal complications of GDM include an increased risk of developing type 2 diabetes later in life, as well as hypertension and preeclampsia during pregnancy. Fetal complications may include macrosomia (large birth weight), birth injuries, and an increased risk of developing metabolic disorders later in life. Understanding the demographics, risk factors, and biomarkers associated with GDM is crucial for effective management and prevention strategies. This research aims to address these aspects comprehensively through the analysis of a dataset comprising 600 pregnant women. By exploring the demographics of the dataset and employing data modeling techniques, the study seeks to identify key risk factors associated with GDM. Moreover, by analyzing various biomarkers, the research aims to gain insights into the physiological mechanisms underlying GDM and its implications for maternal and fetal health. The significance of this research lies in its potential to inform clinical practice and public health policies related to GDM. By identifying demographic patterns and risk factors, healthcare providers can better tailor screening and intervention strategies for pregnant women at risk of GDM. Additionally, insights into biomarkers associated with GDM may contribute to the development of novel diagnostic tools and therapeutic approaches. Ultimately, by enhancing our understanding of GDM, this research aims to improve maternal and fetal outcomes and reduce the burden of this condition on healthcare systems and society. However, it’s important to acknowledge the limitations of the dataset used in this study. Further research utilizing larger and more diverse datasets, perhaps employing advanced data analysis techniques such as Power BI, is warranted to corroborate and expand upon the findings of this research. This underscores the ongoing need for continued investigation into GDM to refine our understanding and improve clinical management strategies.
文摘目的利用Power BI Desktop对医院DIP分组明细进行多维度对比分析及数据可视化,寻找医院DIP亏损原因。方法提取某三甲肿瘤专科医院2022年度部分出院结算DIP入组明细数据,通过数据导入、数据类型转换、建立数据关系、数据建模、数据可视化等方法,建立全院、科室、病种等多个维度的可视化动态分析报表。结果Power BI Desktop能有效提升DIP精细化管理水平,提升工作效率,精准分析亏损原因,指导科室进行改进,有效减少医院DIP医保资金亏损。结论Power BI Desktop具有成本低廉,定制化、可视化、自动化程度高,用户界面友好等特点,值得在各医院尤其是信息化程度低、专科运营人员不足、资金预算不足的医院DIP精细化管理中进行推广。
文摘This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of computer science in different fields of study. The technique used in this paper is handling the inadequate Information for citation;it removes the problem of cold start, which is encountered by very many other recommender systems. In this paper, abstracts, the titles, and the Microsoft academic graphs have been used in coming up with the recommendation list for every document, which is used to combine the content-based approaches and the co-citations. Prioritization and the blending of every technique have been allowed by the tuning system parameters, allowing for the authority in results of recommendation versus the paper novelty. In the end, we do observe that there is a direct correlation between the similarity rankings that have been produced by the system and the scores of the participant. The results coming from the associated scrips of analysis and the user survey have been made available through the recommendation system. Managers must gain the required expertise to fully utilize the benefits that come with business intelligence systems [1]. Data mining has become an important tool for managers that provides insights about their daily operations and leverage the information provided by decision support systems to improve customer relationships [2]. Additionally, managers require business intelligence systems that can rank the output in the order of priority. Ranking algorithm can replace the traditional data mining algorithms that will be discussed in-depth in the literature review [3].
文摘为强化心电信号渐变特征、提高时序信号分类精度,基于Bi-LSTM提出了一种融合渐变数据特征的自适应算法IGF+Bi-LSTM(self-adaptive Bi-LSTM based on integrating gradient features)。该算法在一定范围内自适应选取相似程度最高的渐变数据特征,通过数据融合强化渐变特征在网络隐空间的交互,拓展Bi-LSTM信息传递模式;针对时序信号间存在周期性不匹配和强度不一致的问题,提出一种基于差分的改进的B式距离,以刻画数据和不同标签数据全体间的差异度S并自适应调整IGF+Bi-LSTM中的融合系数。实验表明,该算法在ECG数据集上的分类精度达到98.7%,F1值为98.7%,证明了IGF+Bi-LSTM算法的有效性和实用性。
