MALrisk: a machine-learning-based tool to predict imported malaria in returned travellers with fever

Background: Early diagnosis is key to reducing the morbi-mortality associated with P. falciparum malaria among international travellers. However, access to microbiological tests can be challenging for some healthcare settings. Artificial Intelligence could improve the management of febrile traveller...

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Detalles Bibliográficos
Autores: Balerdi Sarasola, Leire, Fleitas, Pedro E., Bottieau, Emmanuel, Genton, Blaise, Petrone, Paula, Muñoz Gutiérrez, José, Camprubí Ferrer, Daniel
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2024
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/215091
Acceso en línea:https://hdl.handle.net/2445/215091
Access Level:acceso abierto
Palabra clave:Malària
Aprenentatge automàtic
Medicina tropical
Viatges
Malaria
Machine learning
Tropical medicine
Travels
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network_name_str España
repository_id_str
spelling MALrisk: a machine-learning-based tool to predict imported malaria in returned travellers with feverBalerdi Sarasola, LeireFleitas, Pedro E.Bottieau, EmmanuelGenton, BlaisePetrone, PaulaMuñoz Gutiérrez, JoséCamprubí Ferrer, DanielMalàriaAprenentatge automàticMedicina tropicalViatgesMalariaMachine learningTropical medicineTravelsBackground: Early diagnosis is key to reducing the morbi-mortality associated with P. falciparum malaria among international travellers. However, access to microbiological tests can be challenging for some healthcare settings. Artificial Intelligence could improve the management of febrile travellers. Methods: Data from a multicentric prospective study of febrile travellers was obtained to build a machine-learning model to predict malaria cases among travellers presenting with fever. Demographic characteristics, clinical and laboratory variables were leveraged as features. Eleven machine-learning classification models were evaluated by 50-fold cross-validation in a Training set. Then, the model with the best performance, defined by the Area Under the Curve (AUC), was chosen for parameter optimization and evaluation in the Test set. Finally, a reduced model was elaborated with those features that contributed most to the model. Results: Out of eleven machine-learning models, XGBoost presented the best performance (mean AUC of 0.98 and a mean F1 score of 0.78). A reduced model (MALrisk) was developed using only six features: Africa as a travel destination, platelet count, rash, respiratory symptoms, hyperbilirubinemia and chemoprophylaxis intake. MALrisk predicted malaria cases with 100% (95%CI 96-100) sensitivity and 72% (95%CI 68-75) specificity. Conclusions: The MALrisk can aid in the timely identification of malaria in non-endemic settings, allowing the initiation of empiric antimalarials and reinforcing the need for urgent transfer in healthcare facilities with no access to malaria diagnostic tests. This resource could be easily scalable to a digital application and could reduce the morbidity associated with late diagnosis.Oxford University Press2024202520242024info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersion21 p.application/pdfhttps://hdl.handle.net/2445/215091Articles publicats en revistes (Medicina)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésVersió postprint del document publicat a: https://doi.org/10.1093/jtm/taae054Journal of Travel Medicine, 2024https://doi.org/10.1093/jtm/taae054(c) International Society of Travel Medicine, 2024info:eu-repo/semantics/openAccessoai:recercat.cat:2445/2150912026-05-29T05:05:01Z
dc.title.none.fl_str_mv MALrisk: a machine-learning-based tool to predict imported malaria in returned travellers with fever
title MALrisk: a machine-learning-based tool to predict imported malaria in returned travellers with fever
spellingShingle MALrisk: a machine-learning-based tool to predict imported malaria in returned travellers with fever
Balerdi Sarasola, Leire
Malària
Aprenentatge automàtic
Medicina tropical
Viatges
Malaria
Machine learning
Tropical medicine
Travels
title_short MALrisk: a machine-learning-based tool to predict imported malaria in returned travellers with fever
title_full MALrisk: a machine-learning-based tool to predict imported malaria in returned travellers with fever
title_fullStr MALrisk: a machine-learning-based tool to predict imported malaria in returned travellers with fever
title_full_unstemmed MALrisk: a machine-learning-based tool to predict imported malaria in returned travellers with fever
title_sort MALrisk: a machine-learning-based tool to predict imported malaria in returned travellers with fever
dc.creator.none.fl_str_mv Balerdi Sarasola, Leire
Fleitas, Pedro E.
Bottieau, Emmanuel
Genton, Blaise
Petrone, Paula
Muñoz Gutiérrez, José
Camprubí Ferrer, Daniel
author Balerdi Sarasola, Leire
author_facet Balerdi Sarasola, Leire
Fleitas, Pedro E.
Bottieau, Emmanuel
Genton, Blaise
Petrone, Paula
Muñoz Gutiérrez, José
Camprubí Ferrer, Daniel
author_role author
author2 Fleitas, Pedro E.
Bottieau, Emmanuel
Genton, Blaise
Petrone, Paula
Muñoz Gutiérrez, José
Camprubí Ferrer, Daniel
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Malària
Aprenentatge automàtic
Medicina tropical
Viatges
Malaria
Machine learning
Tropical medicine
Travels
topic Malària
Aprenentatge automàtic
Medicina tropical
Viatges
Malaria
Machine learning
Tropical medicine
Travels
description Background: Early diagnosis is key to reducing the morbi-mortality associated with P. falciparum malaria among international travellers. However, access to microbiological tests can be challenging for some healthcare settings. Artificial Intelligence could improve the management of febrile travellers. Methods: Data from a multicentric prospective study of febrile travellers was obtained to build a machine-learning model to predict malaria cases among travellers presenting with fever. Demographic characteristics, clinical and laboratory variables were leveraged as features. Eleven machine-learning classification models were evaluated by 50-fold cross-validation in a Training set. Then, the model with the best performance, defined by the Area Under the Curve (AUC), was chosen for parameter optimization and evaluation in the Test set. Finally, a reduced model was elaborated with those features that contributed most to the model. Results: Out of eleven machine-learning models, XGBoost presented the best performance (mean AUC of 0.98 and a mean F1 score of 0.78). A reduced model (MALrisk) was developed using only six features: Africa as a travel destination, platelet count, rash, respiratory symptoms, hyperbilirubinemia and chemoprophylaxis intake. MALrisk predicted malaria cases with 100% (95%CI 96-100) sensitivity and 72% (95%CI 68-75) specificity. Conclusions: The MALrisk can aid in the timely identification of malaria in non-endemic settings, allowing the initiation of empiric antimalarials and reinforcing the need for urgent transfer in healthcare facilities with no access to malaria diagnostic tests. This resource could be easily scalable to a digital application and could reduce the morbidity associated with late diagnosis.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/215091
url https://hdl.handle.net/2445/215091
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Versió postprint del document publicat a: https://doi.org/10.1093/jtm/taae054
Journal of Travel Medicine, 2024
https://doi.org/10.1093/jtm/taae054
dc.rights.none.fl_str_mv (c) International Society of Travel Medicine, 2024
info:eu-repo/semantics/openAccess
rights_invalid_str_mv (c) International Society of Travel Medicine, 2024
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 21 p.
application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv Articles publicats en revistes (Medicina)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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