Using Machine Learning Algorithms to Identify Key Predictors of Invasive Mold Infection Surveillance
Features identified as important predictors of IMI case status using machine learning methods deviated from classic IMI risk factors. Our results will inform robust and feasible IMI case prediction models for public health surveillance.
Authors
- Robert L. Atmar (ORCID: https://orcid.org/0000-0001-9989-6772)
- Brendan R. Jackson (ORCID: https://orcid.org/0000-0002-3369-2408)
- Zainab Salah (ORCID: https://orcid.org/0000-0001-5577-0756)
- Mitsuru Toda (ORCID: https://orcid.org/0000-0002-5904-6658)
- Sebastian Wurster (ORCID: https://orcid.org/0000-0002-7784-7256)
- Dimitrios P. Kontoyiannis (ORCID: https://orcid.org/0000-0002-8051-2940)
- Carolyn Z. Grimes (ORCID: https://orcid.org/0000-0003-4931-9947)
- Samantha Williams (ORCID: https://orcid.org/0000-0002-8751-9571)
- Tom Chiller (ORCID: https://orcid.org/0000-0002-2251-8033)
- Luis Ostrosky‐Zeichner (ORCID: https://orcid.org/0000-0002-4784-7589)
- José A. Serpa
Institutions
- Centers for Disease Control and Prevention (US)
- The University of Texas MD Anderson Cancer Center (US)
- Baylor College of Medicine (US)
- The University of Texas Health Science Center (US)
- Center for Surveillance, Epidemiology, and Laboratory Services (US)
- The University of Texas Health Science Center at Houston (US)
Publication Details
- Journal
- The Journal of Infectious Diseases
- Published
- 2025-05-31
- DOI
- https://doi.org/10.1093/infdis/jiaf219
- Primary Topic
- Antifungal resistance and susceptibility
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Centers for Disease Control and Prevention