ArticleComputers in biology and medicine2021
PAN-LDA: A latent Dirichlet allocation based novel feature extraction model for COVID-19 data using machine learning.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Assessment of the Efficiency of a ChatGPT-Based Tool, MyGenAssist, in an Industry Pharmacovigilance Department for Case Documentation: Cross-Over Study.Journal of medical Internet research · 2025Article
- Optimizing forensic file classification: enhancing SFCS with βPeerJ. Computer science · 2025Article
- Prognostic prediction models for postoperative patients with stage I to III colorectal cancer based on machine learning.World journal of gastrointestinal oncology · 2024Article
- How perceived sustainability influences consumers' clothing preferences.Scientific reports · 2024Article
- Hot Topic Recognition of Health Rumors Based on Anti-Rumor Articles on the WeChat Official Account Platform: Topic Modeling.Journal of medical Internet research · 2023Article
- Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images.Expert systems with applications · 2023Article
- A deep feature-level fusion model for masked face identity recommendation system.Journal of ambient intelligence and humanized computing · 2022Article
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2 authors.
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Abstract
The recent outbreak of novel Coronavirus disease or COVID-19 is declared a pandemic by the World Health Organization (WHO). The availability of social media platforms has played a vital role in providing and obtaining information about any ongoing event. However, consuming a vast amount of online textual data to predict an event's trends can be troublesome. To our knowledge, no study analyzes the online news articles and the disease data about coronavirus disease. Therefore, we propose an LDA-based topic model, called PAN-LDA (Pandemic-Latent Dirichlet allocation), that incorporates the COVID-19 cases data and news articles into common LDA to obtain a new set of features. The generated features are introduced as additional features to Machine learning(ML) algorithms to improve the forecasting of time series data. Furthermore, we are employing collapsed Gibbs sampling (CGS) as the underlying technique for parameter inference. The results from experiments suggest that the obtained features from PAN-LDA generate more identifiable topics and empirically add value to the outcome.
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