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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 |