Development of a machine learning model to predict risk of development of COVID-19-associated mucormycosis.
Rajashri PatilSahjid MukhidaJyoti AjagundeUzair KhanSameena KhanNageswari GandhamChanda VyawhareNikunja K DasShahzad MirzaPublished in: Future microbiology (2024)
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.
Keyphrases
- machine learning
- big data
- artificial intelligence
- coronavirus disease
- sars cov
- end stage renal disease
- deep learning
- chronic kidney disease
- ejection fraction
- prognostic factors
- newly diagnosed
- risk factors
- type diabetes
- intensive care unit
- blood brain barrier
- brain injury
- mechanical ventilation
- extracorporeal membrane oxygenation