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.

Prognosis prediction in covid-19 patients from lab tests and x-ray data through randomized decision trees

Gerevini A. E.;Maroldi R.;Olivato M.;Putelli L.;Serina I.
2020-01-01

Abstract

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.
2020
CEUR Workshop Proceedings
Ateneo di appartenenza
Esperti anonimi
Inglese
no
5th International Workshop on Knowledge Discovery in Healthcare Data, KDH 2020
2020
esp
Internazionale
ELETTRONICO
2675
27
34
8
CEUR-WS
no
none
Gerevini, A. E.; Maroldi, R.; Olivato, M.; Putelli, L.; Serina, I.
273
info:eu-repo/semantics/conferenceObject
5
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11379/535662
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