Learning prognostic models using a mixture of biclustering and triclustering: predicting the need for non-invasive ventilation in amyotrophic lateral sclerosis

Detalhes bibliográficos
Autor(a) principal: Soares, Diogo F.
Data de Publicação: 2022
Outros Autores: Henriques, Rui, Gromicho, Marta, Carvalho, Mamede, Madeira, Sara C.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10451/54489
Resumo: © 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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spelling Learning prognostic models using a mixture of biclustering and triclustering: predicting the need for non-invasive ventilation in amyotrophic lateral sclerosisAmyotrophic Lateral SclerosisBiclusteringDisease progression patternsPrognosticThree-way dataTriclustering© 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)Longitudinal cohort studies to study disease progression generally combine temporal features produced under periodic assessments (clinical follow-up) with static features associated with single-time assessments, genetic, psychophysiological, and demographic profiles. Subspace clustering, including biclustering and triclustering stances, enables the discovery of local and discriminative patterns from such multidimensional cohort data. These patterns, highly interpretable, are relevant to identifying groups of patients with similar traits or progression patterns. Despite their potential, their use for improving predictive tasks in clinical domains remains unexplored. In this work, we propose to learn predictive models from static and temporal data using discriminative patterns, obtained via biclustering and triclustering, as features within a state-of-the-art classifier, thus enhancing model interpretation. triCluster is extended to find time-contiguous triclusters in temporal data (temporal patterns) and a biclustering algorithm to discover coherent patterns in static data. The transformed data space, composed of bicluster and tricluster features, capture local and cross-variable associations with discriminative power, yielding unique statistical properties of interest. As a case study, we applied our methodology to follow-up data from Portuguese patients with Amyotrophic Lateral Sclerosis (ALS) to predict the need for non-invasive ventilation (NIV) since the last appointment. The results showed that, in general, our methodology outperformed baseline results using the original features. Furthermore, the bicluster/tricluster-based patterns used by the classifier can be used by clinicians to understand the models by highlighting relevant prognostic patterns.This work was partially supported by Fundação para a Ciência e a Tecnologia (FCT), Portugal, the Portuguese public agency for science, technology and innovation, funding to projects AIpALS (PTDC/CCI-CIF/4613/2020), LASIGE (UIDB/ 00408/2020 and UIDP/00408/2020) and INESC-ID (UIDB/ 50021/2020) Research Units, and PhD research scholarship (2020.05100.BD) to DFS; and by the BRAINTEASER project which has received funding from the European Union’s Horizon 2020 research and innovation programme, under the grant agreement No 101017598.ElsevierRepositório da Universidade de LisboaSoares, Diogo F.Henriques, RuiGromicho, MartaCarvalho, MamedeMadeira, Sara C.2022-09-16T15:25:21Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/54489engJ Biomed Inform. 2022 Aug 30;134:1041721532-046410.1016/j.jbi.2022.1041721532-0480info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T17:00:53Zoai:repositorio.ul.pt:10451/54489Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:05:18.374264Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Learning prognostic models using a mixture of biclustering and triclustering: predicting the need for non-invasive ventilation in amyotrophic lateral sclerosis
title Learning prognostic models using a mixture of biclustering and triclustering: predicting the need for non-invasive ventilation in amyotrophic lateral sclerosis
spellingShingle Learning prognostic models using a mixture of biclustering and triclustering: predicting the need for non-invasive ventilation in amyotrophic lateral sclerosis
Soares, Diogo F.
Amyotrophic Lateral Sclerosis
Biclustering
Disease progression patterns
Prognostic
Three-way data
Triclustering
title_short Learning prognostic models using a mixture of biclustering and triclustering: predicting the need for non-invasive ventilation in amyotrophic lateral sclerosis
title_full Learning prognostic models using a mixture of biclustering and triclustering: predicting the need for non-invasive ventilation in amyotrophic lateral sclerosis
title_fullStr Learning prognostic models using a mixture of biclustering and triclustering: predicting the need for non-invasive ventilation in amyotrophic lateral sclerosis
title_full_unstemmed Learning prognostic models using a mixture of biclustering and triclustering: predicting the need for non-invasive ventilation in amyotrophic lateral sclerosis
title_sort Learning prognostic models using a mixture of biclustering and triclustering: predicting the need for non-invasive ventilation in amyotrophic lateral sclerosis
author Soares, Diogo F.
author_facet Soares, Diogo F.
Henriques, Rui
Gromicho, Marta
Carvalho, Mamede
Madeira, Sara C.
author_role author
author2 Henriques, Rui
Gromicho, Marta
Carvalho, Mamede
Madeira, Sara C.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Soares, Diogo F.
Henriques, Rui
Gromicho, Marta
Carvalho, Mamede
Madeira, Sara C.
dc.subject.por.fl_str_mv Amyotrophic Lateral Sclerosis
Biclustering
Disease progression patterns
Prognostic
Three-way data
Triclustering
topic Amyotrophic Lateral Sclerosis
Biclustering
Disease progression patterns
Prognostic
Three-way data
Triclustering
description © 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
publishDate 2022
dc.date.none.fl_str_mv 2022-09-16T15:25:21Z
2022
2022-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10451/54489
url http://hdl.handle.net/10451/54489
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv J Biomed Inform. 2022 Aug 30;134:104172
1532-0464
10.1016/j.jbi.2022.104172
1532-0480
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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