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Abstract Motifs are the fundamental components of complex systems. The topological structure of networks representing complex systems and the frequency and distribution of motifs in these networks are intertwined. The complexities associated with graph and subgraph isomorphism problems, as the core of frequent subgraph mining, directly impact the performance of motif discovery algorithms. Researchers have adopted different strategies for candidate generation and enumeration and frequency computation to cope with these complexities. Besides, in the past few years, there has been an increasing interest in the analysis and mining of temporal networks. In contrast to their static counterparts, these networks change over time in the form of insertion, deletion or substitution of edges or vertices or their attributes. In this article, we provide a survey of motif discovery algorithms proposed in the literature for mining static and temporal networks and review the corresponding algorithms based on their adopted strategies for candidate generation and frequency computation. As we witness the generation of a large amount of network data in social media platforms, bioinformatics applications and communication and transportation networks and the advance in distributed computing and big data technology, we also conduct a survey on the algorithms proposed to resolve the CPU-bound and I/O bound problems in mining static and temporal networks. 

It is shown that appropriate therapeutic management at early stages of sepsis are crucial for preventing further deterioration and irreversible organ damage. Although previous studies considered the cellular and physiological responses as the components of sepsis-related predictive models, temporal connections among the responses have not been widely studied. The objective of this study is to investigate simultaneous changes in cellular and physiological responses represented by 16 clinical variables contributing to seven organ system dysfunctions in patients with sepsis to predict in-hospital mortality. Organ dysfunctions were represented by undirected weighted network models composed of: i) nodes (i.e., 16 clinical variables and three biomarkers including procalcitonin, C-reactive protein, and sedimentation rate), ii) edges (i.e., connection between pair of nodes representing simultaneous dysfunctions), and iii) weights representing the persistence of the co-occurrence of two dysfunctions. Data was collected from 13,367 adult patients (corresponding to 17,953 visits) admitted to the study hospital from July 1, 2013, to December 31, 2015. The study population were categorized based on clinical criteria representing sepsis progression to identify different subpopulations. The findings quantify the optimal window for defining the simultaneity of two dysfunctions, the network properties corresponding to different subpopulations, the discriminatory patterns of simultaneous dysfunctions among subpopulations and in-hospital mortality prediction. The results show that the level of persistence of simultaneous dysfunctions are subpopulation-specific. Insights from this study regarding optimal thresholds of the persistence and combination of simultaneous organ dysfunctions can inform policies to personalize the in-hospital mortality prediction. 

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. 

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. 

Off-label drug use is quite common in clinical practice and inevitable to some extent. Such uses might deliver effective treatment and suggest clinical innovation sometimes, however, they have the unknown risk to cause serious outcomes due to lacking scientific support. As gaining information about off-label drug use could present a clue to the stakeholders such as healthcare professionals and medication manufacturers to further the investigation on drug efficacy and safety, it raises the need to develop a systematic way to detect off-label drug uses. Considering the increasing discussions in online health communities (OHCs) among the health consumers, we proposed to harness the large volume of timely information in OHCs to develop an automated method for detecting off-label drug uses from health consumer generated data. From the text corpus, we extracted medical entities (diseases, drugs, and adverse drug reactions) with lexicon-based approaches and measured their interactions with word embedding models, based on which, we constructed a heterogeneous healthcare network. We defined several meta-path-based indicators to describe the drug-disease associations in the heterogeneous network and used them as features to train a binary classifier built on Random Forest algorithm, to recognize the known drug-disease associations. The classification model obtained better results when incorporating word embedding features and achieved the best performance when using both association rule mining features and word embedding features, with F1-score reaching 0.939, based on which, we identified 2,125 possible off-label drug uses and checked their potential by searching evidence in PubMed and FAERS. 

Off-label drug use refers to using marketed drugs for indications that are not listed in their FDA labeling information. Such uses are very common and sometimes inevitable in clinical practice. To some extent, off-label drug uses provide a pathway for clinical innovation, however, they could cause serious adverse effects due to lacking scientific research and tests. Since identifying the off-label uses can provide a clue to the stakeholders including healthcare providers, patients, and medication manufacturers to further the investigation on drug efficacy and safety, it raises the demand for a systematic way to detect off-label uses. Given data contributed by health consumers in online health communities (OHCs), we developed an automated approach to detect off-label drug uses based on heterogeneous network mining. We constructed a heterogeneous healthcare network with medical entities (e.g. disease, drug, adverse drug reaction) mined from the text corpus, which involved 50 diseases, 1,297 drugs, and 185 ADRs, and determined 13 meta paths between the drugs and diseases. We developed three metrics to represent the meta-path-based topological features. With the network features, we trained the binary classifiers built on Random Forest algorithm to recognize the known drug-disease associations. The best classification model that used lift to measure path weights obtained F1-score of 0.87, based on which, we identified 1,009 candidates of off-label drug uses and examined their potential by searching evidence from PubMed and FAERS.