A machine learning approach for early identification of patients with severe imported malaria

A machine learning approach for early identification of patients with severe imported malaria

Abstract Background The aim of this study is to design ad hoc malaria learning (ML) approaches to predict clinical outcome in all patients with imported malaria and, therefore, to identify the best clinical setting. Methods This is a single-centre cross-sectional study, patients with confirmed malar...

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Bibliographic Details
Main Authors: Alessandra D’Abramo, Francesco Rinaldi, Serena Vita, Riccardo Mazzieri, Angela Corpolongo, Claudia Palazzolo, Tommaso Ascoli Bartoli, Francesca Faraglia, Maria Letizia Giancola, Enrico Girardi, Emanuele Nicastri
Format: Article
Language:English
Published: BMC 2024-02-01
Series:Malaria Journal
Subjects:
Imported malaria
Machine learning
Severe malaria
Risk factors
Online Access:https://doi.org/10.1186/s12936-024-04869-3

Internet

https://doi.org/10.1186/s12936-024-04869-3

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