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نتیجه جستجو - اطلاع رسانی پزشکی

تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 User recommendation in healthcare social media by assessing user similarity in heterogeneous network
توصیه های کاربر در رسانه های اجتماعی سلامت با ارزیابی شباهت کاربر در شبکه های ناهمگن-2017
Objective: The rapid growth of online health social websites has captured a vast amount of healthcare information and made the information easy to access for health consumers. E-patients often use these social websites for informational and emotional support. However, health consumers could be easily overwhelmed by the overloaded information. Healthcare information searching can be very difficult for consumers, not to mention most of them are not skilled information searcher. In this work, we investigate the approaches for measuring user similarity in online health social websites. By recommending similar users to consumers, we can help them to seek informational and emotional support in a more efficient way. Methods: We propose to represent the healthcare social media data as a heterogeneous healthcare infor mation network and introduce the local and global structural approaches for measuring user similarity in a heterogeneous network. We compare the proposed structural approaches with the content-based approach. Results: Experiments were conducted on a dataset collected from a popular online health social website, and the results showed that content-based approach performed better for inactive users, while structural approaches performed better for active users. Moreover, global structural approach outperformed local structural approach for all user groups. In addition, we conducted experiments on local and global struc tural approaches using different weight schemas for the edges in the network. Leverage performed the best for both local and global approaches. Finally, we integrated different approaches and demonstrated that hybrid method yielded better performance than the individual approach. Conclusion: The results indicate that content-based methods can effectively capture the similarity of inac tive users who usually have focused interests, while structural methods can achieve better performance when rich structural information is available. Local structural approach only considers direct connec tions between nodes in the network, while global structural approach takes the indirect connections into account. Therefore, the global similarity approach can deal with sparse networks and capture the implicit similarity between two users. Different approaches may capture different aspects of the similarity rela tionship between two users. When we combine different methods together, we could achieve a better performance than using each individual method.
Keywords: Heterogeneous network mining | Similarity analysis | Healthcare informatics | Social media analytics | Recommendation systems
مقاله انگلیسی
2 Omic and Electronic Health Record Big Data Analytics for Precision Medicine
OMIC و تجزیه و تحلیل داده های بزرگ پرونده الکترونیک سلامت برای پزشکی دقیق -2017
Objective: Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of –omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of healthcare. Methods: In this paper, we present –omic and EHR data characteristics, associated challenges, and data analytics including data preprocessing, mining, and modeling. Results: To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating –omic information into EHR. Conclusion: Big data analytics is able to address –omic and EHR data challenges for paradigm shift toward precision medicine. Significance: Big data analytics makes sense of –omic and EHR data to improve healthcare outcome. It has long lasting societal impact.
Index Terms: Big data analytics | bioinformatics |elec tronic health records (EHRs) | health informatics | –omic data | precision medicine
مقاله انگلیسی
3 Tutorials: Tutorial I: HPC and big data analytics in biomedical informatics
آموزش: آموزش 1: HPC و تجزیه و تحلیل داده های بزرگ در انفورماتیک پزشکی-2016
These tutorials discuss the following: Big Data analysis; biomedical informatics; heterogeneous memory architectures; many-core platform; Internet of Things; high-performance computing; heterogeneous computing infrastructures; Scalarm platform; and OpenCL 2.0.
Keywords: Bioinformatics | Tutorials | Big data | Biological system modeling | Computational modeling | Biomedical informatics
مقاله انگلیسی
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