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Advisor(s)
Abstract(s)
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
Description
Keywords
Cancer Depression Survivorship Federated learning Artifcial intelligence Wearables Remote assessment Quality of life
Citation
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
Publisher
BioMed Central Ltd.