Development of machine learning-based mpox surveillance models in a learning health system
ML and DL models based on clinical notes show promise for identifying mpox cases. In this study, LASSO regression outperformed DL models and excelled in minimising false positives. These findings highlight the potential for ML and DL methods to support case surveillance for mpox and other infectious diseases. These methods may also prove helpful for flagging missed or delayed diagnoses as part of continuous quality improvement.
Authors
- Emma Tucker (ORCID: https://orcid.org/0000-0003-3009-192X)
- Jason Zucker (ORCID: https://orcid.org/0000-0001-6987-6412)
- Noémie Elhadad (ORCID: https://orcid.org/0000-0001-9721-5240)
- Harry Reyes Nieva (ORCID: https://orcid.org/0000-0001-7774-2561)
- Jacob McLean (ORCID: https://orcid.org/0000-0002-4961-3487)
- Clare DeLaurentis (ORCID: https://orcid.org/0000-0002-8239-2583)
- Shauna Gunaratne
Institutions
- NewYork–Presbyterian Hospital (US)
- Harvard University (US)
- Columbia University Irving Medical Center (US)
- Columbia University (US)
Publication Details
- Journal
- Sexually Transmitted Infections
- Published
- 2025-05-02
- DOI
- https://doi.org/10.1136/sextrans-2024-056382
- Citations
- 2
- Primary Topic
- Poxvirus research and outbreaks
- Type
- article
- Field-Weighted Citation Impact
- 2.07
Funders
- National Institutes of Health