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A prospective observational study for a Federated Artificial Intelligence solution for moniToring mental Health status after cancer treatment (FAITH)

2022-12Artigo científico Acesso aberto
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dc.contributor.authorLemos, Raquel
dc.contributor.authorAreias-Marques, Sofia
dc.contributor.authorFerreira, Pedro
dc.contributor.authorO’Brien, Philip
dc.contributor.authorBeltrán-Jaunsarás, María Eugenia
dc.contributor.authorRibeiro, Gabriela
dc.contributor.authorMartín, Miguel
dc.contributor.authordel Monte-Millán, María
dc.contributor.authorLópez-Tarruella, Sara
dc.contributor.authorMassarrah, Tatiana
dc.contributor.authorLuís-Ferreira, Fernando
dc.contributor.authorFrau, Giuseppe
dc.contributor.authorVenios, Stefanos
dc.contributor.authorMcManus, Gary
dc.contributor.authorOliveira-Maia, Albino J.
dc.contributor.authorOliveira-Maia, AJ
dc.contributor.institutionFaculdade de Ciências e Tecnologia (FCT)
dc.contributor.institutionNOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)
dc.contributor.pblBioMed Central (BMC)
dc.date.accessioned2023-03-07T22:27:42Z
dc.date.available2023-03-07T22:27:42Z
dc.date.issued2022-12
dc.descriptionFunding Information: AJO-M was national coordinator for Portugal of a non-interventional study (EDMS-ERI-143085581, 4.0) to characterize a Treatment-Resistant Depression Cohort in Europe, sponsored by Janssen-Cilag, Ltd (2019–2020), is recipient of a grant from Schuhfried GmBH for norming and validation of cognitive tests, and is national coordinator for Portugal of trials of psilocybin therapy for treatment-resistant depression, sponsored by Compass Pathways, Ltd (EudraCT number 2017–003288-36), and of esketamine for treatment-resistant depression, sponsored by Janssen-Cilag, Ltd (EudraCT NUMBER: 2019–002992-33). Funding Information: The FAITH project is funded under the European Commission (EC) Horizon Europe Programme, ‘H2020-EU.3.1.—SOCIETAL CHALLENGES—Health, demographic change, and well-being’. It is funded to the value €4.8 M, under the specific topic ‘SC1-DTH-01–2019—Big data and Artificial Intelligence for monitoring health status and quality of life after the cancer treatment’ with Grant agreement ID: 875358. The funder has no influence in the design, collection, analysis, data interpretation, or manuscript writing. Funding Information: RL is supported by an individual Scientific Employment Stimulus from Fundação para a Ciência e Tecnologia, Portugal (CEECIND/04157/2018). Publisher Copyright: © 2022, The Author(s).
dc.description.abstractBackground: Depression is a common condition among cancer patients, across several points in the disease trajectory. 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 technologies. 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 prospective 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 federated learning framework, enabling to build machine learning models for the prediction and monitoring of depression 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 effective, it will provide healthcare systems with a novel and innovative method to screen depressive symptoms in oncological settings. Trial registration: Trial ID: ISRCTN10423782. Date registered: 21/03/2022.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent2700410
dc.identifier.doi10.1186/s12888-022-04446-5
dc.identifier.issn1471-244X
dc.identifier.otherPURE: 53599221
dc.identifier.otherPURE UUID: 68295480-386b-4cb3-b2af-d5dfea6fad62
dc.identifier.otherScopus: 85144482642
dc.identifier.otherPubMed: 36544126
dc.identifier.otherWOS: 000901958200002
dc.identifier.urihttp://hdl.handle.net/10362/150156
dc.identifier.urlhttps://www.scopus.com/pages/publications/85144482642
dc.language.isoeng
dc.peerreviewedyes
dc.subjectArtificial intelligence
dc.subjectCancer
dc.subjectDepression
dc.subjectFederated learning
dc.subjectQuality of life
dc.subjectRemote assessment
dc.subjectSurvivorship
dc.subjectWearables
dc.subjectPsychiatry and Mental health
dc.subjectSDG 3 - Good Health and Well-being
dc.titleA prospective observational study for a Federated Artificial Intelligence solution for moniToring mental Health status after cancer treatment (FAITH)en
dc.title.subtitlestudy protocolen
dc.typejournal article
degois.publication.issue1
degois.publication.titleBMC Psychiatry
degois.publication.volume22
dspace.entity.typePublication
person.familyNameOliveira-Maia
person.givenNameAlbino Jorge
person.identifier.ciencia-idED15-6691-FEC7
person.identifier.orcid0000-0001-5071-3007
person.identifier.ridB-7976-2009
person.identifier.scopus-author-id14060820700
rcaap.rightsopenAccess
relation.isAuthorOfPublication402bb6ca-e5db-422a-9d43-a807d4076d38
relation.isAuthorOfPublication.latestForDiscovery402bb6ca-e5db-422a-9d43-a807d4076d38

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