Work

Identifying Knowledge Gaps in Heart Failure Research Among Women Using Unsupervised machine-learning Methods

Year: 2021

Type: article

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. Lay abstract There is evidence that heart failure (HF) affects men and women differently. This means that researchers should consider gender when studying HF. However, women, especially postmenopausal women, are underrepresented in HF research. With the advancement in technology, we were able to identify and review almost all published studies on HF among women. Then, we summarized the topics of those studies. We observed that the co-occurrence of atrial fibrillation and HF in women has not been well-studied. We also found that the number of articles studying HF in postmenopausal women was very small compared with those studying women in general. Stress-induced cardiomyopathy, a rare condition, was found to be the most understudied topic. Therefore, more research should be conducted in these areas.

Source: Future Cardiology

DOI: 10.2217/fca-2020-0083