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2021 | OriginalPaper | Buchkapitel

Length of Stay Prediction for Northern Italy COVID-19 Patients Based on Lab Tests and X-Ray Data

verfasst von : Mattia Chiari, Alfonso E. Gerevini, Roberto Maroldi, Matteo Olivato, Luca Putelli, Ivan Serina

Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges

Verlag: Springer International Publishing

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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.

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We chose 2, 4, 6, 8, 10 days after the hospitalisation but any other sequence could be considered.

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Titel: Length of Stay Prediction for Northern Italy COVID-19 Patients Based on Lab Tests and X-Ray Data
verfasst von: Mattia Chiari
Alfonso E. Gerevini
Roberto Maroldi
Matteo Olivato
Luca Putelli
Ivan Serina
Verlag: Springer International Publishing
Buch: Pattern Recognition. ICPR International Workshops and Challenges
Print ISBN: 978-3-030-68762-5

Electronic ISBN: 978-3-030-68763-2

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-68763-2_16

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