Construction of Urban Intelligent Traffic Sharing Information Platform Based on GIS

2020 ◽  
Author(s):  
Chunling Ding ◽  
Yan Chen ◽  
YunFeng Chen
Keyword(s):  
Information Platform ◽  
Traffic Sharing

The effects of information platform-based nursing on preventing venous thromboembolism (VTE) in patients with hip fractures (Preprint)

2020 ◽  
Author(s):  
Yuan Gao ◽  
Xiaojie Fu ◽  
Mingxing Lei ◽  
Pengbin Yin ◽  
Qingmei Wang ◽  
...  
Keyword(s):  
Hip Fracture ◽  
Hip Fractures ◽  
Subgroup Analysis ◽  
Nursing Intervention ◽  
Control Group ◽  
Study Group ◽  
Drug Compliance ◽  
Information Platform ◽  
The Difference ◽  
Length Of Hospitalization

BACKGROUND Mobile apps are becoming increasingly relevant to health care. Apps have been used to improve symptoms, quality of life, and adherence for oral drugs in patients with cancers, pregnancy, or chronic diseases, and the results were satisfying . OBJECTIVE This study aims to develop an information platform with the help of a mobile app and then evaluate whether information platform-based nursing can improve patient’s drug compliance and reduce the incidence of VTE in patients with hip fractures. METHODS We retrospectively analyzed hip fracture patients performed with traditional prevention and intervention of VTE (control group) between January 2008 and November 2012, and prospectively analyzed hip fracture patients conducted with nursing intervention based on the information platform (study group) between January 2016 and September 2017. The information platform can be divided into medical and nursing care end and the patient’s end. Based on the information platform, we could implement risk assessments, monitoring management and early warnings, preventions and treatments, health educations, follow-up and other aspects of nursing interventions for patients. We compared basic characteristics, outcomes including drug compliance, VTE occurrence, and mean length of hospitalization between the two groups. Besides, a subgroup analysis was performed in the study group according to different drug compliances. RESULTS Regarding baseline data, patients in the study group had more morbidities than those in the control group (P<0.05). The difference of drug compliance between the two groups was statistically significant (P<0.001): 64.7% of the patients in the control group had poor drug compliance and only 6.1% patients had poor drug compliance in the study group. In terms of VTE, 126 patients (10.7%) in the control group had VTE, while only 35 patients (7.1%) in the study group had VTE, and the difference was statistically significant (P=0.024). Moreover, the average length of hospitalization in the study group was also significantly lower than that in the control group (10.4 d vs. 13.7 d, P=0.000). Subgroup analysis of the study group showed that the incidence of VTE in patients with non-compliance, partial compliance, and good compliance was 56.7%, 5.8% and 2.8%, respectively (P=0.000). CONCLUSIONS Poor drug compliance leads to higher VTE occurrence. The information platform-based nursing can effectively improve the compliance of patients with hip fracture and thus significantly reduce the incidence of VTE.

scholarly journals A Densely Connected GRU Neural Network Based on Coattention Mechanism for Chinese Rice-Related Question Similarity Matching

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1307
Author(s):  
Haoriqin Wang ◽  
Huaji Zhu ◽  
Huarui Wu ◽  
Xiaomin Wang ◽  
Xiao Han ◽  
...  
Keyword(s):  
Related Question ◽  
Similarity Matching ◽  
Frequency Method ◽  
Text Data ◽  
Information Platform ◽  
Document Frequency ◽  
Proposed Model ◽  
Related Text ◽  
Text Features ◽  
Matching Models

In the question-and-answer (Q&A) communities of the “China Agricultural Technology Extension Information Platform”, thousands of rice-related Chinese questions are newly added every day. The rapid detection of the same semantic question is the key to the success of a rice-related intelligent Q&A system. To allow the fast and automatic detection of the same semantic rice-related questions, we propose a new method based on the Coattention-DenseGRU (Gated Recurrent Unit). According to the rice-related question characteristics, we applied word2vec with the TF-IDF (Term Frequency–Inverse Document Frequency) method to process and analyze the text data and compare it with the Word2vec, GloVe, and TF-IDF methods. Combined with the agricultural word segmentation dictionary, we applied Word2vec with the TF-IDF method, effectively solving the problem of high dimension and sparse data in the rice-related text. Each network layer employed the connection information of features and all previous recursive layers’ hidden features. To alleviate the problem of feature vector size increasing due to dense splicing, an autoencoder was used after dense concatenation. The experimental results show that rice-related question similarity matching based on Coattention-DenseGRU can improve the utilization of text features, reduce the loss of features, and achieve fast and accurate similarity matching of the rice-related question dataset. The precision and F1 values of the proposed model were 96.3% and 96.9%, respectively. Compared with seven other kinds of question similarity matching models, we present a new state-of-the-art method with our rice-related question dataset.

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