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

Cancer Epidemiology, Cancer Surveillance and Infodemiology (77) Innovations and Technology in Cancer Care (423) Participatory Medicine & E-Patients (773)

Published on in Vol 9 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37141, first published .
Using Shopping Data to Improve the Diagnosis of Ovarian Cancer: Computational Analysis of a Web-Based Survey

Using Shopping Data to Improve the Diagnosis of Ovarian Cancer: Computational Analysis of a Web-Based Survey

Using Shopping Data to Improve the Diagnosis of Ovarian Cancer: Computational Analysis of a Web-Based Survey

Authors of this article:

Elizabeth H Dolan1 Author Orcid Image ;   James Goulding1 Author Orcid Image ;   Laila J Tata2 Author Orcid Image ;   Alexandra R Lang3 Author Orcid Image
Article Authors Cited by (4) Tweetations (51) Metrics

Journals

  1. Dexter J, Brubaker L, Bitler B, Goff B, Menon U, Moore K, Sundaram K, Walsh C, Guntupalli S, Behbakht K. Ovarian cancer think tank: An overview of the current status of ovarian cancer screening and recommendations for future directions. Gynecologic Oncology Reports 2024;53:101376 View
  2. Wilson G, Brewer H, Flanagan J, von Wagner C, Hirst Y, Cao C. How Do Patients Use Self-Care to Manage Nonspecific Symptoms Prior to a Cancer Diagnosis? A Rapid Review to Inform Future Interventions to Reduce Delays in Presentation to Primary Care. European Journal of Cancer Care 2024;2024:1 View
  3. Skatova A. Overcoming biases of individual level shopping history data in health research. npj Digital Medicine 2024;7(1) View

Books/Policy Documents

  1. Lang A, Dolan E, Tata L, Goulding J. Convergence: Breaking Down Barriers Between Disciplines. View