Accuracy of symptom checker for the diagnosis of sexually transmitted infections using machine learning and Bayesian network algorithms
Both machine learning and Bayesian models could predict correct diagnoses with reasonable accuracy using prospectively collected data for 12 STIs and other common anogenital conditions. Further work should expand the number of anogenital conditions and seek ways to improve the accuracy, potentially using patient collected images to supplement questionnaire data.
Authors
- Owen Woodberry (ORCID: https://orcid.org/0009-0009-0831-2707)
- Mark Chung (ORCID: https://orcid.org/0000-0002-7529-3878)
- Eric P. F. Chow (ORCID: https://orcid.org/0000-0003-1766-0657)
- Jason J. Ong (ORCID: https://orcid.org/0000-0001-5784-7403)
- Nyi Nyi Soe (ORCID: https://orcid.org/0009-0001-4554-3549)
- Phyu Mon Latt (ORCID: https://orcid.org/0000-0001-5880-5731)
- Christopher Kincaid Fairley (ORCID: https://orcid.org/0000-0001-9081-1664)
- Janet M Towns (ORCID: https://orcid.org/0000-0003-2714-4680)
- David Lee (ORCID: https://orcid.org/0009-0006-5423-4525)
- Lei Zhang (ORCID: https://orcid.org/0000-0003-2343-084X)
Institutions
- The University of Melbourne (AU)
- Xi'an Honghui Hospital (CN)
- Melbourne Sexual Health Centre (AU)
- Monash Health (AU)
- Alfred Health (AU)
- Monash University (AU)
- Xi'an Jiaotong University (CN)
Publication Details
- Journal
- BMC Infectious Diseases
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1186/s12879-024-10285-4
- Citations
- 4
- Primary Topic
- Reproductive tract infections research
- Type
- article
- Field-Weighted Citation Impact
- 1.38
Funders
- Monash University
- National Health and Medical Research Council