Personalized prediction of one-year mental health deterioration using adaptive learning algorithms

Detalhes bibliográficos
Autor(a) principal: Kourou, Konstantina
Data de Publicação: 2023
Outros Autores: 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.
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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spelling 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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