SOURCE-LINKED INTELLIGENCE

Monitoring mentAl healTh in brEast canceR

CORDIS · observation · Publication date unknown

mportant symptoms in the deterioration of the mental health of these women. We also assume that automatically estimating these symptoms using voice descriptors extracted from real-life recordings and machine learning pipelines will make it easier to monitor them in the patients' homes. Finally, we hypothesize that the use of a Bayesian network algorithm combining the symptom network and the voice-based symptom estimations will allow a more accurate joint estimation of these symptoms - and thus improve the identification and monitoring of mental health-related symptoms in women with breast cancer. The interdisciplinary MATER project is based on Colive Voice, a unique dataset of clinical and voice data and leverages both the complementary host's and supervisor's extensive experience in digital and personalized health and the applicant's knowledge of vocal biomarker design and machine learning, mental disorder semiology, and Bayesian networks. This project will allow the applicant to improve his skills in voice signal processing, precision health (in particular in oncology), but also i

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recordType

award

status

TERMINATED

region

EU

value

175920

unit

EUR