Prognosis Prediction in Covid-19 Patients from Lab Tests and X-ray Data through Randomized Decision Trees
This provides a tool for healthcare professionals to assess patient prognosis in COVID-19, but it is incremental as it applies existing methods to a new dataset.
The paper tackled predicting the risk of death in hospitalized COVID-19 patients using demographic, X-ray, and lab data, achieving good performance with ensembles of decision trees tested on over 2000 patients.
AI and Machine Learning can offer powerful tools to help in the fight against Covid-19. In this paper we present a study and a concrete tool based on machine learning to predict the prognosis of hospitalised patients with Covid-19. In particular we address the task of predicting the risk of death of a patient at different times of the hospitalisation, on the base of some demographic information, chest X-ray scores and several laboratory findings. Our machine learning models use ensembles of decision trees trained and tested using data from more than 2000 patients. An experimental evaluation of the models shows good performance in solving the addressed task.