Development of a machine learning model to predict risk of development of COVID-19-associated mucormycosis.

Autor: Patil R; Department of Microbiology, Dr DY Patil Medical College Hospital & Research Centre, Dr DY Patil Vidyapeeth, Pimpri, Pune 18, India., Mukhida S; Department of Microbiology, Dr DY Patil Medical College Hospital & Research Centre, Dr DY Patil Vidyapeeth, Pimpri, Pune 18, India., Ajagunde J; Department of Microbiology, Dr DY Patil Medical College Hospital & Research Centre, Dr DY Patil Vidyapeeth, Pimpri, Pune 18, India., Khan U; Department of Microbiology, Dr DY Patil Medical College Hospital & Research Centre, Dr DY Patil Vidyapeeth, Pimpri, Pune 18, India., Khan S; Department of Microbiology, Dr DY Patil Medical College Hospital & Research Centre, Dr DY Patil Vidyapeeth, Pimpri, Pune 18, India., Gandham N; Department of Microbiology, Dr DY Patil Medical College Hospital & Research Centre, Dr DY Patil Vidyapeeth, Pimpri, Pune 18, India., Vyawhare C; Department of Microbiology, Dr DY Patil Medical College Hospital & Research Centre, Dr DY Patil Vidyapeeth, Pimpri, Pune 18, India., Das NK; Department of Microbiology, Dr DY Patil Medical College Hospital & Research Centre, Dr DY Patil Vidyapeeth, Pimpri, Pune 18, India., Mirza S; Department of Microbiology, Dr DY Patil Medical College Hospital & Research Centre, Dr DY Patil Vidyapeeth, Pimpri, Pune 18, India.
Jazyk: angličtina
Zdroj: Future microbiology [Future Microbiol] 2024 Mar; Vol. 19, pp. 297-305. Date of Electronic Publication: 2024 Jan 31.
DOI: 10.2217/fmb-2023-0190
Abstrakt: Aim: The study aimed to identify quantitative parameters that increase the risk of rhino-orbito-cerebral mucormycosis, and subsequently developed a machine learning model that can anticipate susceptibility to developing this condition. Methods: Clinicopathological data from 124 patients were used to quantify their association with COVID-19-associated mucormycosis (CAM) and subsequently develop a machine learning model to predict its likelihood. Results: Diabetes mellitus, noninvasive ventilation and hypertension were found to have statistically significant associations with radiologically confirmed CAM cases. Conclusion: Machine learning models can be used to accurately predict the likelihood of development of CAM, and this methodology can be used in creating prediction algorithms of a wide variety of infections and complications.
Databáze: MEDLINE
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