Machine and deep learning for longitudinal biomedical data: a review of methods and applications

Exploiting existing longitudinal data cohorts can bring enormous benefits to the medical field, as many diseases have a complex and multi-factorial time-course, and start to develop long before symptoms appear. With the increasing healthcare digitisation, the application of machine learning techniqu...

Descripción completa

Detalles Bibliográficos
Autores: Cascarano, Anna, Mur Petit, Jordi, Hernández-González, Jerónimo, Camacho, Marina, Toro Eadie, Nina de, Gkontra, Polyxeni, Chadeau-Hyam, Marc, Vitrià i Marca, Jordi, Lekadir, Karim, 1977-
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
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:2445/220657
Acceso en línea:https://hdl.handle.net/2445/220657
Access Level:acceso abierto
Palabra clave:Dades massives
Aprenentatge automàtic
Ciències de la salut
Big data
Machine learning
Medical sciences
id ES_e67dcbccecdb12cf95fdea28d38486ee
oai_identifier_str oai:recercat.cat:2445/220657
network_acronym_str ES
network_name_str España
repository_id_str
spelling Machine and deep learning for longitudinal biomedical data: a review of methods and applicationsCascarano, AnnaMur Petit, JordiHernández-González, JerónimoCamacho, MarinaToro Eadie, Nina deGkontra, PolyxeniChadeau-Hyam, MarcVitrià i Marca, JordiLekadir, Karim, 1977-Dades massivesAprenentatge automàticCiències de la salutBig dataMachine learningMedical sciencesExploiting existing longitudinal data cohorts can bring enormous benefits to the medical field, as many diseases have a complex and multi-factorial time-course, and start to develop long before symptoms appear. With the increasing healthcare digitisation, the application of machine learning techniques for longitudinal biomedical data may enable the development of new tools for assisting clinicians in their day-to-day medical practice, such as for early diagnosis, risk prediction, treatment planning and prognosis estimation. However, due to the heterogeneity and complexity of time-varying data sets, the development of suitable machine learning models introduces major challenges for data scientists as well as for clinical researchers. This paper provides a comprehensive and critical review of recent developments and applications in machine learning for longitudinal biomedical data. Although the paper provides a discussion of clustering methods, its primary focus is on the prediction of static outcomes, defined as the value of the event of interest at a given instant in time, using longitudinal features, which has emerged as the most commonly employed approach in healthcare applications. First, the main approaches and algorithms for building longitudinal machine learning models are presented in detail, including their technical implementations, strengths and limitations. Subsequently, most recent biomedical and clinical applications are reviewed and discussed, showing promising results in a wide range of medical specialties. Lastly, we discuss current challenges and consider future directions in the field to enhance the development of machine learning tools from longitudinal biomedical data.Springer Verlag2025202520232025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion61 p.application/pdfhttps://hdl.handle.net/2445/220657Articles publicats en revistes (Matemàtiques i Informàtica)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1007/s10462-023-10561-wArtificial Intelligence Review, 2023, vol. 56, p. 1711-1771https://doi.org/10.1007/s10462-023-10561-wcc by (c) Anna Cascarano et al., 2023http://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/2206572026-05-29T05:05:01Z
dc.title.none.fl_str_mv Machine and deep learning for longitudinal biomedical data: a review of methods and applications
title Machine and deep learning for longitudinal biomedical data: a review of methods and applications
spellingShingle Machine and deep learning for longitudinal biomedical data: a review of methods and applications
Cascarano, Anna
Dades massives
Aprenentatge automàtic
Ciències de la salut
Big data
Machine learning
Medical sciences
title_short Machine and deep learning for longitudinal biomedical data: a review of methods and applications
title_full Machine and deep learning for longitudinal biomedical data: a review of methods and applications
title_fullStr Machine and deep learning for longitudinal biomedical data: a review of methods and applications
title_full_unstemmed Machine and deep learning for longitudinal biomedical data: a review of methods and applications
title_sort Machine and deep learning for longitudinal biomedical data: a review of methods and applications
dc.creator.none.fl_str_mv Cascarano, Anna
Mur Petit, Jordi
Hernández-González, Jerónimo
Camacho, Marina
Toro Eadie, Nina de
Gkontra, Polyxeni
Chadeau-Hyam, Marc
Vitrià i Marca, Jordi
Lekadir, Karim, 1977-
author Cascarano, Anna
author_facet Cascarano, Anna
Mur Petit, Jordi
Hernández-González, Jerónimo
Camacho, Marina
Toro Eadie, Nina de
Gkontra, Polyxeni
Chadeau-Hyam, Marc
Vitrià i Marca, Jordi
Lekadir, Karim, 1977-
author_role author
author2 Mur Petit, Jordi
Hernández-González, Jerónimo
Camacho, Marina
Toro Eadie, Nina de
Gkontra, Polyxeni
Chadeau-Hyam, Marc
Vitrià i Marca, Jordi
Lekadir, Karim, 1977-
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Dades massives
Aprenentatge automàtic
Ciències de la salut
Big data
Machine learning
Medical sciences
topic Dades massives
Aprenentatge automàtic
Ciències de la salut
Big data
Machine learning
Medical sciences
description Exploiting existing longitudinal data cohorts can bring enormous benefits to the medical field, as many diseases have a complex and multi-factorial time-course, and start to develop long before symptoms appear. With the increasing healthcare digitisation, the application of machine learning techniques for longitudinal biomedical data may enable the development of new tools for assisting clinicians in their day-to-day medical practice, such as for early diagnosis, risk prediction, treatment planning and prognosis estimation. However, due to the heterogeneity and complexity of time-varying data sets, the development of suitable machine learning models introduces major challenges for data scientists as well as for clinical researchers. This paper provides a comprehensive and critical review of recent developments and applications in machine learning for longitudinal biomedical data. Although the paper provides a discussion of clustering methods, its primary focus is on the prediction of static outcomes, defined as the value of the event of interest at a given instant in time, using longitudinal features, which has emerged as the most commonly employed approach in healthcare applications. First, the main approaches and algorithms for building longitudinal machine learning models are presented in detail, including their technical implementations, strengths and limitations. Subsequently, most recent biomedical and clinical applications are reviewed and discussed, showing promising results in a wide range of medical specialties. Lastly, we discuss current challenges and consider future directions in the field to enhance the development of machine learning tools from longitudinal biomedical data.
publishDate 2023
dc.date.none.fl_str_mv 2023
2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/220657
url https://hdl.handle.net/2445/220657
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1007/s10462-023-10561-w
Artificial Intelligence Review, 2023, vol. 56, p. 1711-1771
https://doi.org/10.1007/s10462-023-10561-w
dc.rights.none.fl_str_mv cc by (c) Anna Cascarano et al., 2023
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc by (c) Anna Cascarano et al., 2023
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 61 p.
application/pdf
dc.publisher.none.fl_str_mv Springer Verlag
publisher.none.fl_str_mv Springer Verlag
dc.source.none.fl_str_mv Articles publicats en revistes (Matemàtiques i Informàtica)
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
_version_ 1869422771378847744
score 15.811543

Similares en LA Referencia