Publicação
Learning prognostic models using a mixture of biclustering and triclustering: predicting the need for non-invasive ventilation in amyotrophic lateral sclerosis
| Resumo: | 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. |
|---|---|
| Autores principais: | Soares, Diogo F. |
| Outros Autores: | Henriques, Rui; Gromicho, Marta; Carvalho, Mamede; Madeira, Sara C. |
| Assunto: | Amyotrophic Lateral Sclerosis Biclustering Disease progression patterns Prognostic Three-way data Triclustering |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório da Universidade de Lisboa |
| _version_ | 1866810386625331200 |
|---|---|
| author | Soares, Diogo F. |
| author2 | Henriques, Rui Gromicho, Marta Carvalho, Mamede Madeira, Sara C. |
| author2_role | author author author author |
| author_facet | Soares, Diogo F. Henriques, Rui Gromicho, Marta Carvalho, Mamede Madeira, Sara C. |
| author_role | author |
| contributor_name_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Soares, Diogo F.\",\"Person.identifier.orcid\":\"0000-0003-3503-0755\"},{\"Person.name\":\"Henriques, Rui\",\"Person.identifier.orcid\":\"0000-0002-3993-0171\"},{\"Person.name\":\"Gromicho, Marta\",\"Person.identifier.orcid\":\"0000-0003-2111-4579\"},{\"Person.name\":\"Carvalho, Mamede\",\"Person.identifier.orcid\":\"0000-0001-7556-0158\"},{\"Person.name\":\"Madeira, Sara C.\",\"Person.identifier.orcid\":\"0000-0002-1459-8096\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| datacite.creators.creator.creatorName.fl_str_mv | Soares, Diogo F. Henriques, Rui Gromicho, Marta Carvalho, Mamede Madeira, Sara C. |
| datacite.date.Accepted.fl_str_mv | 2022-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2022-09-16T15:25:21Z |
| datacite.date.embargoed.fl_str_mv | 2022-09-16T15:25:21Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Amyotrophic Lateral Sclerosis Biclustering Disease progression patterns Prognostic Three-way data Triclustering |
| datacite.titles.title.fl_str_mv | Learning prognostic models using a mixture of biclustering and triclustering: predicting the need for non-invasive ventilation in amyotrophic lateral sclerosis |
| dc.contributor.none.fl_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| dc.creator.none.fl_str_mv | Soares, Diogo F. Henriques, Rui Gromicho, Marta Carvalho, Mamede Madeira, Sara C. |
| dc.date.Accepted.fl_str_mv | 2022-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2022-09-16T15:25:21Z |
| dc.date.embargoed.fl_str_mv | 2022-09-16T15:25:21Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10451/54489 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Elsevier |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Amyotrophic Lateral Sclerosis Biclustering Disease progression patterns Prognostic Three-way data Triclustering |
| dc.title.fl_str_mv | Learning prognostic models using a mixture of biclustering and triclustering: predicting the need for non-invasive ventilation in amyotrophic lateral sclerosis |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | 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. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://repositorio.ulisboa.pt/bitstreams/4cf2cf6b-b2df-4f00-8cb6-988af3b90d11/download |
| funding.funder.alternateName_str_mv | FCT FCT FCT FCT EC |
| funding.funder.identifier_str_mv | http://doi.org/10.13039/501100001871 http://doi.org/10.13039/501100001871 http://doi.org/10.13039/501100001871 http://doi.org/10.13039/501100001871 http://doi.org/10.13039/501100008530 |
| funding.funder.name_str_mv | Fundação para a Ciência e a Tecnologia Fundação para a Ciência e a Tecnologia Fundação para a Ciência e a Tecnologia Fundação para a Ciência e a Tecnologia European Commission |
| funding.name_str_mv | 6817 - DCRRNI ID 6817 - DCRRNI ID 6817 - DCRRNI ID H2020 |
| id | ul_4cb362b8fded8d0ea8dcf8fec493db1f |
| identifier.url.fl_str_mv | http://hdl.handle.net/10451/54489 |
| instacron_str | ul |
| institution | Universidade de Lisboa |
| instname_str | Universidade de Lisboa |
| language | eng |
| network_acronym_str | ul |
| network_name_str | Repositório da Universidade de Lisboa |
| oai_identifier_str | oai:repositorio.ulisboa.pt:10451/54489 |
| organization_str_mv | urn:organizationAcronym:ul |
| person_str_mv | Soares, Diogo F. Soares, Diogo F. https://www.ciencia-id.pt/D713-C08C-A84A D713-C08C-A84A http://orcid.org/0000-0003-3503-0755 0000-0003-3503-0755 Henriques, Rui Henriques, Rui https://www.ciencia-id.pt/3D1F-D220-28D1 3D1F-D220-28D1 http://orcid.org/0000-0002-3993-0171 0000-0002-3993-0171 Gromicho, Marta Gromicho, Marta https://www.ciencia-id.pt/EA1D-1979-5C3A EA1D-1979-5C3A http://orcid.org/0000-0003-2111-4579 0000-0003-2111-4579 Carvalho, Mamede Carvalho, Mamede http://orcid.org/0000-0001-7556-0158 0000-0001-7556-0158 Madeira, Sara C. Madeira, Sara C. https://www.ciencia-id.pt/AF12-AA0D-0C7B AF12-AA0D-0C7B http://orcid.org/0000-0002-1459-8096 0000-0002-1459-8096 |
| publishDate | 2022 |
| publisher.none.fl_str_mv | Elsevier |
| reponame_str | Repositório da Universidade de Lisboa |
| repository_id_str | urn:repositoryAcronym:ul |
| service_str_mv | urn:repositoryAcronym:ul |
| spelling | engElsevierpt_PTLongitudinal 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.application/pdfpt_PTLearning prognostic models using a mixture of biclustering and triclustering: predicting the need for non-invasive ventilation in amyotrophic lateral sclerosisPersonalSoares, Diogo F.DSpacehttp://dspace.org/items/1b0f1562-0792-4cdc-b787-e624c71acd06DSpacehttp://dspace.org/items/1b0f1562-0792-4cdc-b787-e624c71acd06SoaresDiogoCiência IDhttps://www.ciencia-id.ptD713-C08C-A84AORCIDhttp://orcid.org0000-0003-3503-0755PersonalHenriques, RuiDSpacehttp://dspace.org/items/e9b43d60-75aa-48ca-88d5-45a85b8d8b7dDSpacehttp://dspace.org/items/e9b43d60-75aa-48ca-88d5-45a85b8d8b7dHenriquesRuiCiência IDhttps://www.ciencia-id.pt3D1F-D220-28D1ORCIDhttp://orcid.org0000-0002-3993-0171PersonalGromicho, MartaDSpacehttp://dspace.org/items/c6f0ac72-fdd9-499d-87e2-f13aa1545565DSpacehttp://dspace.org/items/c6f0ac72-fdd9-499d-87e2-f13aa1545565SilvaMarta Luísa Gromicho MorgadoCiência IDhttps://www.ciencia-id.ptEA1D-1979-5C3AORCIDhttp://orcid.org0000-0003-2111-4579Scopus Author IDhttps://www.scopus.com6506583274PersonalCarvalho, MamedeDSpacehttp://dspace.org/items/dd7f55d4-c2b5-4fd2-9bd1-a9542a62f58fDSpacehttp://dspace.org/items/dd7f55d4-c2b5-4fd2-9bd1-a9542a62f58fde CarvalhoMamedeORCIDhttp://orcid.org0000-0001-7556-0158Scopus Author IDhttps://www.scopus.com7101893769PersonalMadeira, Sara C.DSpacehttp://dspace.org/items/f6c1f830-a944-47e3-a471-51014c18932eDSpacehttp://dspace.org/items/f6c1f830-a944-47e3-a471-51014c18932eMadeiraSaraCiência IDhttps://www.ciencia-id.ptAF12-AA0D-0C7BORCIDhttp://orcid.org0000-0002-1459-8096Researcher IDhttps://www.researcherid.comC-5494-2008Scopus Author IDhttps://www.scopus.com6602138051HostingInstitutionOrganizationalRepositório Científico de Acesso Aberto da ULisboae-mailmailto:repositorio@reitoria.ulisboa.ptrepositorio@reitoria.ulisboa.ptISSNIsPartOf1532-0464DOIIsPartOf10.1016/j.jbi.2022.1041722022-09-16T15:25:21Z20222022-01-01T00:00:00ZHandlehttp://hdl.handle.net/10451/54489http://purl.org/coar/access_right/c_abf2open accessAmyotrophic Lateral SclerosisBiclusteringDisease progression patternsPrognosticThree-way dataTriclustering2195742 bytesFundação para a Ciência e a TecnologiaLASIGE - Extreme Computing6817 - DCRRNI IDCrossref Funder IDhttp://doi.org/10.13039/501100001871Fundação para a Ciência e a TecnologiaLASIGE - Extreme Computing6817 - DCRRNI IDCrossref Funder IDhttp://doi.org/10.13039/501100001871Fundação para a Ciência e a TecnologiaInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa6817 - DCRRNI IDCrossref Funder IDhttp://doi.org/10.13039/501100001871Fundação para a Ciência e a TecnologiaEfficient Algorithms for Temporal Triclustering: Targeting the Challenges of Temporality for Effective Solutions in Biomedical ApplicationsCrossref Funder IDhttp://doi.org/10.13039/501100001871European CommissionBRinging Artificial INTelligencE home for a better cAre of amyotrophic lateral sclerosis and multiple SclERosisH2020Crossref Funder IDhttp://doi.org/10.13039/501100008530literaturehttp://purl.org/coar/resource_type/c_6501journal article2022http://creativecommons.org/licenses/by-nc-nd/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://repositorio.ulisboa.pt/bitstreams/4cf2cf6b-b2df-4f00-8cb6-988af3b90d11/downloadJournal of Biomedical Informatics134 |
| 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 |
| status | SINGLETON |
| subject.fl_str_mv | Amyotrophic Lateral Sclerosis Biclustering Disease progression patterns Prognostic Three-way data Triclustering |
| title | 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_short | 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 |
| topic | Amyotrophic Lateral Sclerosis Biclustering Disease progression patterns Prognostic Three-way data Triclustering |
| topic_facet | Amyotrophic Lateral Sclerosis Biclustering Disease progression patterns Prognostic Three-way data Triclustering |
| url | http://hdl.handle.net/10451/54489 |
| visible | 1 |