Khalid Alhussain1, Kazuhiko Kido2, Nilanjana Dwibedi3, Traci LeMasters3, Danielle E Rose4, Ranjita Misra5, Usha Sambamoorthi6. 1. Department of Pharmacy Practice, College of Clinical Pharmacy, King Faisal University, Al-Ahsa, Kingdom of Saudi Arabia. 2. Department of Clinical Pharmacy, School of Pharmacy, West Virginia University, Morgantown, WV 26506, USA. 3. Department of Pharmaceutical Systems and Policy, School of Pharmacy, West Virginia University, Morgantown, WV 26506, USA. 4. HSR&D Center for the Study of Healthcare Innovation, Implementation, and Policy, VA Greater Los Angeles Healthcare System, Sepulveda, CA 91343, USA. 5. Department of Social & Behavioral Sciences, School of Public Health, West Virginia University, Morgantown, WV 26505, USA. 6. Department of Pharmacotherapy, HSC College of Pharmacy, The University of North Texas Health Science Center, Fort Worth, TX 76107, USA.
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.
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.
Authors: Emelia J Benjamin; Paul Muntner; Alvaro Alonso; Marcio S Bittencourt; Clifton W Callaway; April P Carson; Alanna M Chamberlain; Alexander R Chang; Susan Cheng; Sandeep R Das; Francesca N Delling; Luc Djousse; Mitchell S V Elkind; Jane F Ferguson; Myriam Fornage; Lori Chaffin Jordan; Sadiya S Khan; Brett M Kissela; Kristen L Knutson; Tak W Kwan; Daniel T Lackland; Tené T Lewis; Judith H Lichtman; Chris T Longenecker; Matthew Shane Loop; Pamela L Lutsey; Seth S Martin; Kunihiro Matsushita; Andrew E Moran; Michael E Mussolino; Martin O'Flaherty; Ambarish Pandey; Amanda M Perak; Wayne D Rosamond; Gregory A Roth; Uchechukwu K A Sampson; Gary M Satou; Emily B Schroeder; Svati H Shah; Nicole L Spartano; Andrew Stokes; David L Tirschwell; Connie W Tsao; Mintu P Turakhia; Lisa B VanWagner; John T Wilkins; Sally S Wong; Salim S Virani Journal: Circulation Date: 2019-03-05 Impact factor: 29.690
Authors: Richard K Cheng; Margueritte Cox; Megan L Neely; Paul A Heidenreich; Deepak L Bhatt; Zubin J Eapen; Adrian F Hernandez; Javed Butler; Clyde W Yancy; Gregg C Fonarow Journal: Am Heart J Date: 2014-07-22 Impact factor: 4.749
Authors: Kevin A Bybee; Tomas Kara; Abhiram Prasad; Amir Lerman; Greg W Barsness; R Scott Wright; Charanjit S Rihal Journal: Ann Intern Med Date: 2004-12-07 Impact factor: 25.391
Authors: Paul A Heidenreich; Nancy M Albert; Larry A Allen; David A Bluemke; Javed Butler; Gregg C Fonarow; John S Ikonomidis; Olga Khavjou; Marvin A Konstam; Thomas M Maddox; Graham Nichol; Michael Pham; Ileana L Piña; Justin G Trogdon Journal: Circ Heart Fail Date: 2013-04-24 Impact factor: 8.790