This paper is in the following e-collection/theme issue:

Crowdsourcing and Mechanical Turks (91) New Methods (909) Diabetes Health Services and System Innovations (104)

Published on in Vol 19, No 6 (2017): June

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

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

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

Authors of this article:

Christopher John Brady1 Author Orcid Image ;   Lucy Iluka Mudie1 Author Orcid Image ;   Xueyang Wang1 Author Orcid Image ;   Eliseo Guallar2 Author Orcid Image ;   David Steven Friedman1, 2 Author Orcid Image
Article Authors Cited by (10) Tweetations (6) Metrics

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;18(2):113 View
  9. Maloca P, Pfau M, Janeschitz‐Kriegl L, Reich M, Goerdt L, Holz F, Müller P, Valmaggia P, Fasler K, Keane P, Zarranz‐Ventura J, Zweifel S, Wiesendanger J, Kaiser P, Enz T, Rothenbuehler S, Hasler P, Juedes M, Freichel C, Egan C, Tufail A, Scholl H, Denk N. Human selection bias drives the linear nature of the more ground truth effect in explainable deep learning optical coherence tomography image segmentation. Journal of Biophotonics 2024;17(2) View

Books/Policy Documents

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