ArticleNetwork modeling and analysis in health informatics and bioinformatics2021
Community and topic modeling for infectious disease clinical trial recommendation.
Article in Network modeling and analysis in health informatics and bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinical trials are crucial for the advancement of treatment and knowledge within the medical community. Although the ClinicalTrials.gov initiative has resulted in a rich source of information for clinical trial research, only a handful of analytic studies have been carried out to understand this valuable data source. Analysis of this database provides insight for emerging trends of clinical research. In this study, we propose to use network analysis to understand infectious disease clinical trial research. Our goal is to understand two important issues related to the clinical trials: (1) the concentrations and characteristics of infectious disease clinical trial research, and (2) recommendation of clinical trials to a sponsor (or an investigator). The first issue helps summarize clinical trial research related to a particular disease(s), and the second issue helps match clinical trial sponsors and investigators for information recommendation. By using 4228 clinical trials as the test bed, our study investigates 4864 sponsors and 1879 research areas characterized by Medical Subject Heading (MeSH) keywords. We use a network to characterize infectious disease clinical trials, and design a new community-topic-based link prediction approach to predict sponsors' interests. Our design relies on network modeling of both clinical trial sponsors and keywords. For sponsors, we extract communities with each community consisting of sponsors with coherent interests. For keywords, we extract topics with each topic containing semantic consistent keywords. The communities and topics are combined for accurate clinical trial recommendation. This transformative study concludes that using network analysis can tremendously help the understanding of clinical trial research for effective summarization, characterization, and prediction.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.