Predicting outcomes for patients hospitalized with COVID-19
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1362Keywords:
Explainable, COVID-19 Outcome, Machine LearningAbstract
Objective: This study aims to evaluate the effectiveness of Machine Learning (ML) models in predicting outcomes for patients diagnosed with COVID-19 considering data from medical records and exams. Method: Several ML algorithms and Explainable techniques were investigated on clinical evolution data of patients admitted to the Pedro Ernesto University Hospital (HUPE) during the years 2020 and 2021. Results: The Random Forest model was found to be the most efficient, with an accuracy of 74% in the validation dataset. In addition, techniques based on Explainable Artificial Intelligence show that changes in the number of rods and the prescription of noradrenaline were the variables that had the greatest impact on predicting outcomes. Conclusion: The results encourage healthcare institutions to use methods based on decision support to organize or even prioritize care for their patients
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