Community and topic modeling for infectious disease clinical trial recommendation

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 com munities 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.
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - Category: Bioinformatics Source Type: research