A prospective observational study for a Federated Artificial Intelligence solution for monitoring mental health status after cancer treatment (FAITH): study protocol
Autor(a) principal: | |
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Data de Publicação: | 2022 |
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/10400.12/8918 |
Resumo: | Background: Depression is a common condition among cancer patients, across several points in the disease trajec‑ tory. Although presenting higher prevalence rates than the general population, it is often not reported or remains unnoticed. Moreover, somatic symptoms of depression are common in the oncological context and should not be dismissed as a general symptom of cancer. It becomes even more challenging to track psychological distress in the period after the treatment, where connection with the healthcare system typically becomes sporadic. The main goal of the FAITH project is to remotely identify and predict depressive symptoms in cancer survivors, based on a federated machine learning (ML) approach, towards optimization of privacy. Methods: FAITH will remotely analyse depression markers, predicting their negative trends. These markers will be treated in distinct categories, namely nutrition, sleep, activity and voice, assessed in part through wearable technolo‑ gies. The study will include 300 patients who have had a previous diagnosis of breast or lung cancer and will be recruited 1 to 5 years after the end of primary cancer. The study will be organized as a 12-month longitudinal pro‑ spective observational cohort study, with monthly assessments to evaluate depression symptoms and quality of life among cancer survivors. The primary endpoint is the severity of depressive symptoms as measured by the Hamilton Depression Rating Scale (Ham-D) at months 3, 6, 9 and 12. Secondary outcomes include self-reported anxiety and depression symptoms (HADS scale), and perceived quality of life (EORTC questionnaires), at baseline and monthly. Based on the predictive models gathered during the study, FAITH will also aim at further developing a conceptual fed‑ erated learning framework, enabling to build machine learning models for the prediction and monitoring of depres‑ sion without direct access to user’s personal data. Discussion: Improvements in the objectivity of psychiatric assessment are necessary. Wearable technologies can provide potential indicators of depression and anxiety and be used for biofeedback. If the FAITH application is |
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A prospective observational study for a Federated Artificial Intelligence solution for monitoring mental health status after cancer treatment (FAITH): study protocolCancerDepressionSurvivorshipFederated learningArtifcial intelligenceWearablesRemote assessmentQuality of lifeBackground: Depression is a common condition among cancer patients, across several points in the disease trajec‑ tory. Although presenting higher prevalence rates than the general population, it is often not reported or remains unnoticed. Moreover, somatic symptoms of depression are common in the oncological context and should not be dismissed as a general symptom of cancer. It becomes even more challenging to track psychological distress in the period after the treatment, where connection with the healthcare system typically becomes sporadic. The main goal of the FAITH project is to remotely identify and predict depressive symptoms in cancer survivors, based on a federated machine learning (ML) approach, towards optimization of privacy. Methods: FAITH will remotely analyse depression markers, predicting their negative trends. These markers will be treated in distinct categories, namely nutrition, sleep, activity and voice, assessed in part through wearable technolo‑ gies. The study will include 300 patients who have had a previous diagnosis of breast or lung cancer and will be recruited 1 to 5 years after the end of primary cancer. The study will be organized as a 12-month longitudinal pro‑ spective observational cohort study, with monthly assessments to evaluate depression symptoms and quality of life among cancer survivors. The primary endpoint is the severity of depressive symptoms as measured by the Hamilton Depression Rating Scale (Ham-D) at months 3, 6, 9 and 12. Secondary outcomes include self-reported anxiety and depression symptoms (HADS scale), and perceived quality of life (EORTC questionnaires), at baseline and monthly. Based on the predictive models gathered during the study, FAITH will also aim at further developing a conceptual fed‑ erated learning framework, enabling to build machine learning models for the prediction and monitoring of depres‑ sion without direct access to user’s personal data. Discussion: Improvements in the objectivity of psychiatric assessment are necessary. Wearable technologies can provide potential indicators of depression and anxiety and be used for biofeedback. If the FAITH application isBioMed Central Ltd.Repositório do ISPALemos, RaquelAreias-Marques, SofiaFerreira, PedroO’Brien, PhilipBeltrán-Jaunsarás, María EugeniaRibeiro, GabrielaMartín, Migueldel Monte-Millán, MaríaLópez-Tarruella, SaraMassarrah, TatianaLuís-Ferreira, FernandoFrau, GiuseppeVenios, StefanosMcManus, GaryOliveira-Maia, Albino J.2023-01-17T16:03:51Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.12/8918engLemos, R., Areias-Marques, S., Ferreira, P., O’Brien, P., Beltrán-Jaunsarás, M. E., Ribeiro, G., Martín, M., del Monte-Millán, M., López-Tarruella, S., Massarrah, T., Luís-Ferreira, F., Frau, G., Venios, S., McManus, G., & Oliveira-Maia, A. J. (2022). A prospective observational study for a Federated Artificial Intelligence solution for moniToring mental Health status after cancer treatment (FAITH): study protocol. BMC Psychiatry, 22(1), 1–13. https://doi.org/10.1186/s12888-022-04446-51471244X10.1186/s12888-022-04446-5info: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:RCAAP2023-01-22T02:16:42Zoai:repositorio.ispa.pt:10400.12/8918Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:45:30.130005Repositório 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 |
A prospective observational study for a Federated Artificial Intelligence solution for monitoring mental health status after cancer treatment (FAITH): study protocol |
title |
A prospective observational study for a Federated Artificial Intelligence solution for monitoring mental health status after cancer treatment (FAITH): study protocol |
spellingShingle |
A prospective observational study for a Federated Artificial Intelligence solution for monitoring mental health status after cancer treatment (FAITH): study protocol Lemos, Raquel Cancer Depression Survivorship Federated learning Artifcial intelligence Wearables Remote assessment Quality of life |
title_short |
A prospective observational study for a Federated Artificial Intelligence solution for monitoring mental health status after cancer treatment (FAITH): study protocol |
title_full |
A prospective observational study for a Federated Artificial Intelligence solution for monitoring mental health status after cancer treatment (FAITH): study protocol |
title_fullStr |
A prospective observational study for a Federated Artificial Intelligence solution for monitoring mental health status after cancer treatment (FAITH): study protocol |
title_full_unstemmed |
A prospective observational study for a Federated Artificial Intelligence solution for monitoring mental health status after cancer treatment (FAITH): study protocol |
title_sort |
A prospective observational study for a Federated Artificial Intelligence solution for monitoring mental health status after cancer treatment (FAITH): study protocol |
author |
Lemos, Raquel |
author_facet |
Lemos, Raquel Areias-Marques, Sofia Ferreira, Pedro O’Brien, Philip Beltrán-Jaunsarás, María Eugenia Ribeiro, Gabriela Martín, Miguel del Monte-Millán, María López-Tarruella, Sara Massarrah, Tatiana Luís-Ferreira, Fernando Frau, Giuseppe Venios, Stefanos McManus, Gary Oliveira-Maia, Albino J. |
author_role |
author |
author2 |
Areias-Marques, Sofia Ferreira, Pedro O’Brien, Philip Beltrán-Jaunsarás, María Eugenia Ribeiro, Gabriela Martín, Miguel del Monte-Millán, María López-Tarruella, Sara Massarrah, Tatiana Luís-Ferreira, Fernando Frau, Giuseppe Venios, Stefanos McManus, Gary Oliveira-Maia, Albino J. |
author2_role |
author author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório do ISPA |
dc.contributor.author.fl_str_mv |
Lemos, Raquel Areias-Marques, Sofia Ferreira, Pedro O’Brien, Philip Beltrán-Jaunsarás, María Eugenia Ribeiro, Gabriela Martín, Miguel del Monte-Millán, María López-Tarruella, Sara Massarrah, Tatiana Luís-Ferreira, Fernando Frau, Giuseppe Venios, Stefanos McManus, Gary Oliveira-Maia, Albino J. |
dc.subject.por.fl_str_mv |
Cancer Depression Survivorship Federated learning Artifcial intelligence Wearables Remote assessment Quality of life |
topic |
Cancer Depression Survivorship Federated learning Artifcial intelligence Wearables Remote assessment Quality of life |
description |
Background: Depression is a common condition among cancer patients, across several points in the disease trajec‑ tory. Although presenting higher prevalence rates than the general population, it is often not reported or remains unnoticed. Moreover, somatic symptoms of depression are common in the oncological context and should not be dismissed as a general symptom of cancer. It becomes even more challenging to track psychological distress in the period after the treatment, where connection with the healthcare system typically becomes sporadic. The main goal of the FAITH project is to remotely identify and predict depressive symptoms in cancer survivors, based on a federated machine learning (ML) approach, towards optimization of privacy. Methods: FAITH will remotely analyse depression markers, predicting their negative trends. These markers will be treated in distinct categories, namely nutrition, sleep, activity and voice, assessed in part through wearable technolo‑ gies. The study will include 300 patients who have had a previous diagnosis of breast or lung cancer and will be recruited 1 to 5 years after the end of primary cancer. The study will be organized as a 12-month longitudinal pro‑ spective observational cohort study, with monthly assessments to evaluate depression symptoms and quality of life among cancer survivors. The primary endpoint is the severity of depressive symptoms as measured by the Hamilton Depression Rating Scale (Ham-D) at months 3, 6, 9 and 12. Secondary outcomes include self-reported anxiety and depression symptoms (HADS scale), and perceived quality of life (EORTC questionnaires), at baseline and monthly. Based on the predictive models gathered during the study, FAITH will also aim at further developing a conceptual fed‑ erated learning framework, enabling to build machine learning models for the prediction and monitoring of depres‑ sion without direct access to user’s personal data. Discussion: Improvements in the objectivity of psychiatric assessment are necessary. Wearable technologies can provide potential indicators of depression and anxiety and be used for biofeedback. If the FAITH application is |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022 2022-01-01T00:00:00Z 2023-01-17T16:03:51Z |
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/10400.12/8918 |
url |
http://hdl.handle.net/10400.12/8918 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Lemos, R., Areias-Marques, S., Ferreira, P., O’Brien, P., Beltrán-Jaunsarás, M. E., Ribeiro, G., Martín, M., del Monte-Millán, M., López-Tarruella, S., Massarrah, T., Luís-Ferreira, F., Frau, G., Venios, S., McManus, G., & Oliveira-Maia, A. J. (2022). A prospective observational study for a Federated Artificial Intelligence solution for moniToring mental Health status after cancer treatment (FAITH): study protocol. BMC Psychiatry, 22(1), 1–13. https://doi.org/10.1186/s12888-022-04446-5 1471244X 10.1186/s12888-022-04446-5 |
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 |
BioMed Central Ltd. |
publisher.none.fl_str_mv |
BioMed Central Ltd. |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799130926528069632 |