Breast cancer diagnosis has been associated with poor mental health, with significant impairment of quality of life. In order to ensure support for successful adaptation to this illness, it is of paramount importance to identify the most prominent factors affecting well-being that allow for accurate prediction of mental health status across time. Here we exploit a rich set of clinical, psychological, socio-demographic and lifestyle data from a large multicentre study of patients recently diagnosed with breast cancer, in order to classify patients based on their mental health status and further identify potential predictors of such status. For this purpose, a supervised learning pipeline using cross-sectional data was implemented for the formulation of a classification scheme of mental health status 6 months after diagnosis. Model performance in terms of AUC ranged from 0.81± 0.04 to 0.90± 0.03. Several psychological variables, including initial levels of anxiety and depression, emerged as highly predictive of short-term mental health status of women diagnosed with breast cancer.
|Original language||American English|
|Title of host publication||43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|State||Published - 2021|
|Event||43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 - Virtual, Online, Mexico|
Duration: 1 Nov 2021 → 5 Nov 2021
|Name||Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS|
|Conference||43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021|
|Period||1/11/21 → 5/11/21|
Bibliographical noteFunding Information:
This work is funded by the European Commission: Project BOUNCE. This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 777167.
© 2021 IEEE.