Unravelling intubation challenges: a machine learning approach incorporating multiple predictive parameters
Algorithms for machine learning provide insightful information for anticipating challenging airway management. By making it possible to forecast airway difficulties more accurately, these techniques can potentially improve clinical practice and patient outcomes.
Authors
- Parisa Sezari (ORCID: https://orcid.org/0000-0003-3874-1050)
- Kamran Mottaghi (ORCID: https://orcid.org/0000-0003-3371-1047)
- Zahra Kohzadi
- Ali Dabbagh (ORCID: https://orcid.org/0000-0003-4344-8131)
- Alireza Jafari (ORCID: https://orcid.org/0000-0002-9079-1971)
- Zeinab Kohzadi (ORCID: https://orcid.org/0000-0001-8038-5454)
- Shahabedin Rahmatizadeh (ORCID: https://orcid.org/0000-0003-2429-1642)
- Saba Khoshtinatan
Institutions
- Medical University of Ilam (IR)
- Ilam University (IR)
- Shahid Beheshti University of Medical Sciences (IR)
Publication Details
- Journal
- BMC Anesthesiology
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1186/s12871-024-02842-w
- Citations
- 2
- Primary Topic
- Airway Management and Intubation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.28
Funders
- Shahid Beheshti University of Medical Sciences