Text Fingerprinting and Topic Mining in the Prescription Opioid Use Literature

Prescription opioids are powerful pain-reducing medications. Thousands of articles that focus on prescription opioid use (POU) and its associated medical disorders have been published. However, it is time-consuming and labor-intensive to extract and understand the information of all POU-related published articles. In this study, we applied the well-adapted topic modeling method, Latent Dirichlet Allocation (LDA), to perform text mining on POU-related literature. We have collected six large academic abstract datasets by searching PubMed using the Medical Subject Headings (MeSH): prescription opioid, codeine, morphine, hydrocodone, oxycodone, and methadone. We then applied topic modeling to identify topics and analyze topic similarities/differences in these six datasets. Word clouds and histograms were used to depict the distribution of vocabularies over each topic in which the most prevalent words conveyed a topic’s meaning. TreeMap and trend analysis were performed to fingerprint abstracts and explore the prevalent topic dynamics in the POU-related literature. Results showed the ability of topic modeling as a computational tool to segregate a vast quantity of articles into different themes that provide a systematic literature overview. The LDA topics recaptured the search keywords in PubMed and revealed further relevant themes by comparison analysis between different datasets.