Identifying knowledge gaps in heart failure research among women using unsupervised machine-learning methods

Abstract

Aim: To identify knowledge gaps in heart failure (HF) research among women, especially postmenopausal women. Materials & methods: We retrieved HF articles from PubMed. Natural language processing and text mining techniques were used to screen relevant articles and identify study objective(s) from abstracts. After text preprocessing, we performed topic modeling with non-negative matrix factorization to cluster articles based on the primary topic. Clusters were independently validated and labeled by three investigators familiar with HF research. Results: Our model yielded 15 topic clusters from articles on HF among women. Atrial fibrillation was found to be the most understudied topic. From articles specific to postmenopausal women, five clusters were identified. The smallest cluster was about stress-induced cardiomyopathy. Conclusion: Topic modeling can help identify understudied areas in medical research.