Artificial Intelligence (AI) Based Prediction of Mortality, for COVID-19 Patients
<|reference_start|>Artificial Intelligence (AI) Based Prediction of Mortality, for COVID-19 Patients: For severely affected COVID-19 patients, it is crucial to identify high-risk patients and predict survival and need for intensive care (ICU). Most of the proposed models are not well reported making them less reproducible and prone to high risk of bias particularly in presence of imbalance data/class. In this study, the performances of nine machine and deep learning algorithms in combination with two widely used feature selection methods were investigated to predict last status representing mortality, ICU requirement, and ventilation days. Fivefold cross-validation was used for training and validation purposes. To minimize bias, the training and testing sets were split maintaining similar distributions. Only 10 out of 122 features were found to be useful in prediction modelling with Acute kidney injury during hospitalization feature being the most important one. The algorithms performances depend on feature numbers and data pre-processing techniques. LSTM performs the best in predicting last status and ICU requirement with 90%, 92%, 86% and 95% accuracy, sensitivity, specificity, and AUC respectively. DNN performs the best in predicting Ventilation days with 88% accuracy. Considering all the factors and limitations including absence of exact time point of clinical onset, LSTM with carefully selected features can accurately predict last status and ICU requirement. DNN performs the best in predicting Ventilation days. Appropriate machine learning algorithm with carefully selected features and balance data can accurately predict mortality, ICU requirement and ventilation support. Such model can be very useful in emergency and pandemic where prompt and precise<|reference_end|>
@article{tamala2024artificial,
    title={Artificial Intelligence (AI) Based Prediction of Mortality, for COVID-19
  Patients},
    author={Mahbubunnabi Tamala, Mohammad Marufur Rahmanb, Maryam Alhasimc,
  Mobarak Al Mulhimd, Mohamed Derichee},
    journal={arXiv preprint arXiv:2403.19355},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19355},
    primaryClass={cs.LG}
}