Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida
Abstract
Share and Cite
Datta, D.; Ray, S.; Martinez, L.; Newman, D.; Dalmida, S.G.; Hashemi, J.; Sareli, C.; Eckardt, P. Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida. Diagnostics 2024, 14, 1866. https://doi.org/10.3390/diagnostics14171866
Datta D, Ray S, Martinez L, Newman D, Dalmida SG, Hashemi J, Sareli C, Eckardt P. Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida. Diagnostics. 2024; 14(17):1866. https://doi.org/10.3390/diagnostics14171866
Chicago/Turabian StyleDatta, Debarshi, Subhosit Ray, Laurie Martinez, David Newman, Safiya George Dalmida, Javad Hashemi, Candice Sareli, and Paula Eckardt. 2024. "Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida" Diagnostics 14, no. 17: 1866. https://doi.org/10.3390/diagnostics14171866
APA StyleDatta, D., Ray, S., Martinez, L., Newman, D., Dalmida, S. G., Hashemi, J., Sareli, C., & Eckardt, P. (2024). Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida. Diagnostics, 14(17), 1866. https://doi.org/10.3390/diagnostics14171866