Personalized prediction of one-year mental health deterioration using adaptive learning algorithms
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/152743 |
Resumo: | Funding Information: This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 777167. Publisher Copyright: © 2023, The Author(s). |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Personalized prediction of one-year mental health deterioration using adaptive learning algorithmsa multicenter breast cancer prospective studyGeneralSDG 3 - Good Health and Well-beingFunding Information: This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 777167. Publisher Copyright: © 2023, The Author(s).Identifying individual patient characteristics that contribute to long-term mental health deterioration following diagnosis of breast cancer (BC) is critical in clinical practice. The present study employed a supervised machine learning pipeline to address this issue in a subset of data from a prospective, multinational cohort of women diagnosed with stage I–III BC with a curative treatment intention. Patients were classified as displaying stable HADS scores (Stable Group; n = 328) or reporting a significant increase in symptomatology between BC diagnosis and 12 months later (Deteriorated Group; n = 50). Sociodemographic, life-style, psychosocial, and medical variables collected on the first visit to their oncologist and three months later served as potential predictors of patient risk stratification. The flexible and comprehensive machine learning (ML) pipeline used entailed feature selection, model training, validation and testing. Model-agnostic analyses aided interpretation of model results at the variable- and patient-level. The two groups were discriminated with a high degree of accuracy (Area Under the Curve = 0.864) and a fair balance of sensitivity (0.85) and specificity (0.87). Both psychological (negative affect, certain coping with cancer reactions, lack of sense of control/positive expectations, and difficulties in regulating negative emotions) and biological variables (baseline percentage of neutrophils, thrombocyte count) emerged as important predictors of mental health deterioration in the long run. Personalized break-down profiles revealed the relative impact of specific variables toward successful model predictions for each patient. Identifying key risk factors for mental health deterioration is an essential first step toward prevention. Supervised ML models may guide clinical recommendations toward successful illness adaptation.NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)RUNKourou, KonstantinaManikis, GeorgiosMylona, EugeniaPoikonen-Saksela, PaulaMazzocco, KettiPat-Horenczyk, RuthSousa, BertaOliveira-Maia, Albino J.Mattson, JohannaRoziner, IlanPettini, GretaKondylakis, HaridimosMarias, KostasNuutinen, MikkoKarademas, EvangelosSimos, PanagiotisFotiadis, Dimitrios I.2023-05-12T22:12:19Z2023-04-292023-04-29T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/152743eng2045-2322PURE: 60414341https://doi.org/10.1038/s41598-023-33281-1info: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:RCAAP2024-03-11T05:35:15Zoai:run.unl.pt:10362/152743Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:55:03.692735Repositório 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 |
Personalized prediction of one-year mental health deterioration using adaptive learning algorithms a multicenter breast cancer prospective study |
title |
Personalized prediction of one-year mental health deterioration using adaptive learning algorithms |
spellingShingle |
Personalized prediction of one-year mental health deterioration using adaptive learning algorithms Kourou, Konstantina General SDG 3 - Good Health and Well-being |
title_short |
Personalized prediction of one-year mental health deterioration using adaptive learning algorithms |
title_full |
Personalized prediction of one-year mental health deterioration using adaptive learning algorithms |
title_fullStr |
Personalized prediction of one-year mental health deterioration using adaptive learning algorithms |
title_full_unstemmed |
Personalized prediction of one-year mental health deterioration using adaptive learning algorithms |
title_sort |
Personalized prediction of one-year mental health deterioration using adaptive learning algorithms |
author |
Kourou, Konstantina |
author_facet |
Kourou, Konstantina Manikis, Georgios Mylona, Eugenia Poikonen-Saksela, Paula Mazzocco, Ketti Pat-Horenczyk, Ruth Sousa, Berta Oliveira-Maia, Albino J. Mattson, Johanna Roziner, Ilan Pettini, Greta Kondylakis, Haridimos Marias, Kostas Nuutinen, Mikko Karademas, Evangelos Simos, Panagiotis Fotiadis, Dimitrios I. |
author_role |
author |
author2 |
Manikis, Georgios Mylona, Eugenia Poikonen-Saksela, Paula Mazzocco, Ketti Pat-Horenczyk, Ruth Sousa, Berta Oliveira-Maia, Albino J. Mattson, Johanna Roziner, Ilan Pettini, Greta Kondylakis, Haridimos Marias, Kostas Nuutinen, Mikko Karademas, Evangelos Simos, Panagiotis Fotiadis, Dimitrios I. |
author2_role |
author author author author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM) RUN |
dc.contributor.author.fl_str_mv |
Kourou, Konstantina Manikis, Georgios Mylona, Eugenia Poikonen-Saksela, Paula Mazzocco, Ketti Pat-Horenczyk, Ruth Sousa, Berta Oliveira-Maia, Albino J. Mattson, Johanna Roziner, Ilan Pettini, Greta Kondylakis, Haridimos Marias, Kostas Nuutinen, Mikko Karademas, Evangelos Simos, Panagiotis Fotiadis, Dimitrios I. |
dc.subject.por.fl_str_mv |
General SDG 3 - Good Health and Well-being |
topic |
General SDG 3 - Good Health and Well-being |
description |
Funding Information: This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 777167. Publisher Copyright: © 2023, The Author(s). |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-05-12T22:12:19Z 2023-04-29 2023-04-29T00:00:00Z |
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/10362/152743 |
url |
http://hdl.handle.net/10362/152743 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2045-2322 PURE: 60414341 https://doi.org/10.1038/s41598-023-33281-1 |
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.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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1799138138074906624 |