Predicting Emerging Themes in Rapidly Expanding COVID-19 Literature With Unsupervised Word Embeddings and Machine Learning: Evidence-Based Study

• Published in: Journal of medical Internet research Vol. 24; no. 11; p. e34067
• Main Authors: Pal, Ridam, Chopra, Harshita, Awasthi, Raghav, Bandhey, Harsh, Nagori, Aditya, Sethi, Tavpritesh
• Format: Journal Article
• Language: English
• Published: Canada Journal of Medical Internet Research 02.11.2022; Gunther Eysenbach MD MPH, Associate Professor; JMIR Publications
• Subjects: Analysis; Complications; Computational linguistics; Coronaviruses; COVID-19; Data mining; Diagrams; Forecasts and trends; Health aspects; Humans; Language processing; Machine Learning; Medical research; Multimedia; Natural language; Natural language interfaces; Original Paper; Post-Acute COVID-19 Syndrome; Prediction models; Semantics; Supervised Machine Learning; Symptoms; Trends; Vaccines; COVID-19; named entity recognition; machine learning; natural language preprocessing; unsupervised word embeddings

Evidence from peer-reviewed literature is the cornerstone for designing responses to global threats such as COVID-19. In massive and rapidly growing corpuses, such as COVID-19 publications, assimilating and synthesizing information is challenging. Leveraging a robust computational pipeline that eval...