Improving Consensus Scoring of Crowdsourced Data Using the Rasch Model: Development and Refinement of a Diagnostic Instrument

Journals

1. G. Rodrigo E, Aledo J, Gámez J. Machine learning from crowds: A systematic review of its applications. WIREs Data Mining and Knowledge Discovery 2019;9(2) View
2. Créquit P, Mansouri G, Benchoufi M, Vivot A, Ravaud P. Mapping of Crowdsourcing in Health: Systematic Review. Journal of Medical Internet Research 2018;20(5):e187 View
3. Lalor J, Wu H, Chen L, Mazor K, Yu H. ComprehENotes, an Instrument to Assess Patient Reading Comprehension of Electronic Health Record Notes: Development and Validation. Journal of Medical Internet Research 2018;20(4):e139 View
4. Tobore I, Li J, Yuhang L, Al-Handarish Y, Kandwal A, Nie Z, Wang L. Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations. JMIR mHealth and uHealth 2019;7(8):e11966 View
5. Socia D, Brady C, West S, Cockrell R, Vinetz J. Detection of trachoma using machine learning approaches. PLOS Neglected Tropical Diseases 2022;16(12):e0010943 View
6. Brady C, Cockrell R, Aldrich L, Wolle M, West S. A Virtual Reading Center Model Using Crowdsourcing to Grade Photographs for Trachoma: Validation Study. Journal of Medical Internet Research 2023;25:e41233 View
7. Naufal F, West S, Brady C. Utility of photography for trachoma surveys: A systematic review. Survey of Ophthalmology 2022;67(3):842 View
8. Cho J, Zhang W, Armstrong G, Cho D, Culican S. Crowdsourcing and its applications to ophthalmology. Expert Review of Ophthalmology 2023:1 View

Books/Policy Documents

1. Mishra A, Mohapatra S, Bisoy S. Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis. View