An Enhanced Topic Extraction Model for Medical PubMed Documents using State-of-the-Art Algorithms
TL;DR
This paper enhances the four-topic modelling with two optimization algorithms to improve topic extraction and assesses their performance, such as Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN), combined with grid search and Bayesian optimization algorithms.
Abstract
The rapid growth of medical literature databases represents both a challenge and an opportunity for pharmacovigilance. Medical abstracts are full of specialized terms and complex sentences that make extracting meaningful insights on the adverse effects of drugs very challenging. This paper addresses the critical problem of extracting relevant topics related to drug adverse effects from PubMed medical abstracts using advanced topic modelling methods. It enhances the four-topic modelling with two optimization algorithms to improve topic extraction and assesses their performance, such as Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN), combined with grid search and Bayesian optimization algorithms. The experimental results show that LDA optimized with Bayesian optimization gives the highest coherence score, 0.605, which is better than other models. Coherent results, as shown in a complex comparison table, reveal the performance of each model and optimization method.