Off-label drug use is an important healthcare topic as it is quite common and sometimes inevitable in medical practice. Though gaining information about off-label drug uses could benefit a lot of healthcare stakeholders such as patients, physicians, and pharmaceutical companies, there is no such data repository of such information available. There is a desire for a systematic approach to detect off-label drug uses. Other than using data sources such as EHR and clinical notes that are provided by healthcare providers, we exploited social media data especially online health community (OHC) data to detect the off-label drug uses, with consideration of the increasing social media users and the large volume of valuable and timely user-generated contents. We adopted tensor decomposition technique, CP decomposition in this work, to deal with the sparsity and missing data problem in social media data. On the basis of tensor decomposition results, we used two approaches to identify off-label drug use candidates: (1) one is via ranking the CP decomposition resulting components, (2) the other one is applying a heterogeneous network mining method, proposed in our previous work [9], on the reconstructed dataset by CP decomposition. The first approach identified a number of significant off-label use candidates, for which we were able to conduct case studies and found medical explanations for 7 out of 12 identified off-label use candidates. The second approach achieved better performance than the previous method [9] by improving the F1-score by 3%. It demonstrated the effectiveness of performing tensor decomposition on social media data for detecting off-label drug use.
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User recommendation in healthcare social media by assessing user similarity in heterogeneous network
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 information 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 data set 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 structural 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 inactive 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 connections 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 relationship between two users. When we combine different methods together, we could achieve a better performance than using each individual method.
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- Award ID(s):
- 1650531
- NSF-PAR ID:
- 10048043
- Date Published:
- Journal Name:
- Artificial intelligence in medicine
- ISSN:
- 1873-2860
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/http://dx.doi.org/10.1016/j.artmed.2017.03.002
