The recent spread of COVID-19 put a strain on hospitals all over the world. In this paper we address the problem of hospital overloads and present a tool based on machine learning to predict the length of stay of hospitalised patients affected by COVID-19. This tool was developed using Random Forests and Extra Trees regression algorithms and was trained and tested on the data from more than 1000 hospitalised patients from Northern Italy. These data contain demographics, several laboratory test results and a score that evaluates the severity of the pulmonary conditions. The experimental results show good performance for the length of stay prediction and, in particular, for identifying which patients will stay in hospital for a long period of time.
Length of Stay Prediction for Northern Italy COVID-19 Patients Based on Lab Tests and X-Ray Data
Chiari M.;Gerevini A. E.;Maroldi R.;Olivato M.;Serina I.
2021-01-01
Abstract
The recent spread of COVID-19 put a strain on hospitals all over the world. In this paper we address the problem of hospital overloads and present a tool based on machine learning to predict the length of stay of hospitalised patients affected by COVID-19. This tool was developed using Random Forests and Extra Trees regression algorithms and was trained and tested on the data from more than 1000 hospitalised patients from Northern Italy. These data contain demographics, several laboratory test results and a score that evaluates the severity of the pulmonary conditions. The experimental results show good performance for the length of stay prediction and, in particular, for identifying which patients will stay in hospital for a long period of time.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
