TY - GEN
T1 - Prediction of Poor Mental Health Following Breast Cancer Diagnosis Using Random Forests 1
AU - Mylona, Eugenia
AU - Kourou, Konstantina
AU - Manikis, Georgios
AU - Kondylakis, Haridimos
AU - Marias, Kostas
AU - Karademas, Evangelos
AU - Poikonen-Saksela, Paula
AU - Mazzocco, Ketti
AU - Marzorati, Chiara
AU - Pat-Horenczyk, Ruth
AU - Roziner, Ilan
AU - Sousa, Berta
AU - Oliveira-Maia, Albino
AU - Simos, Panagiotis
AU - Fotiadis, Dimitrios I.
N1 - Publisher Copyright: © 2021 IEEE.
PY - 2021/11
Y1 - 2021/11
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85122544335&partnerID=8YFLogxK
U2 - https://doi.org/10.1109/EMBC46164.2021.9629589
DO - https://doi.org/10.1109/EMBC46164.2021.9629589
M3 - منشور من مؤتمر
C2 - 34891626
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1753
EP - 1756
BT - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Y2 - 1 November 2021 through 5 November 2021
ER -
