Community and topic modeling for infectious disease clinical trial recommendation

• Published in: Network modeling and analysis in health informatics and bioinformatics Vol. 10; no. 1; p. 47
• Main Authors: Elkin, Magdalyn E., Zhu, Xingquan
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.12.2021; Springer Nature B.V
• Subjects: Applications of Graph Theory and Complex Networks; Bioinformatics; Clinical trials; Computational Biology/Bioinformatics; Computer Science; Corporate sponsorship; Finite element method; Health Informatics; Infectious diseases; Intervention; Keywords; Medical device industry; Medical equipment; Medical research; Medical Subject Headings-MeSH; Modelling; Network analysis; Original; Original Article; Pharmaceutical industry; Subject heading schemes; Trends; United States > US; Link prediction; Clinical trials; Recommendation; Network community
• ISSN: 2192-6662; 2192-6670
• DOI: 10.1007/s13721-021-00321-7

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.