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

Elizabeth H Dolan   1 , BA, MSc ;   James Goulding   1 , MSc, PhD ;   Laila J Tata   2 , MSc, PhD ;   Alexandra R Lang   3 , PhD

1 Neodemographics Lab, Nottingham University Business School, University of Nottingham, Nottingham, United Kingdom

2 Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, United Kingdom

3 Human Factors, Faculty of Engineering, University of Nottingham, Nottingham, United Kingdom

Corresponding Author:

  • Elizabeth H Dolan, BA, MSc
  • Neodemographics Lab, Nottingham University Business School, University of Nottingham
  • Si Yuan Building, Jubilee Campus
  • Nottingham, NG8 1BB
  • United Kingdom
  • Phone: 44 115 846 6655
  • Email: elizabeth.dolan@nottingham.ac.uk