TY - JOUR
T1 - A machine learning-based pipeline for modeling medical, socio-demographic, lifestyle and self-reported psychological traits as predictors of mental health outcomes after breast cancer diagnosis
T2 - An initial effort to define resilience effects
AU - Kourou, Konstantina
AU - Manikis, Georgios
AU - Poikonen-Saksela, Paula
AU - Mazzocco, Ketti
AU - Pat-Horenczyk, Ruth
AU - Sousa, Berta
AU - Oliveira-Maia, Albino J.
AU - Mattson, Johanna
AU - Roziner, Ilan
AU - Pettini, Greta
AU - Kondylakis, Haridimos
AU - Marias, Kostas
AU - Karademas, Evangelos
AU - Simos, Panagiotis
AU - Fotiadis, Dimitrios I.
PY - 2021/4
Y1 - 2021/4
N2 - Displaying resilience following a diagnosis of breast cancer is crucial for successful adaptation to illness, well-being, and health outcomes. Several theoretical and computational models have been proposed toward understanding the complex process of illness adaptation, involving a large variety of patient sociodemographic, lifestyle, medical, and psychological characteristics. To date, conventional multivariate statistical methods have been used extensively to model resilience. In the present work we describe a computational pipeline designed to identify the most prominent predictors of mental health outcomes following breast cancer diagnosis. A machine learning framework was developed and tested on the baseline data (recorded immediately post diagnosis) from an ongoing prospective, multinational study. This fully annotated dataset includes socio-demographic, lifestyle, medical and self-reported psychological characteristics of women recently diagnosed with breast cancer (N = 609). Nine different feature selection and cross-validated classification schemes were compared on their performance in classifying patients into low vs high depression symptom severity. Best-performing approaches involved a meta-estimator combined with a Support Vector Machines (SVMs) classification algorithm, exhibiting balanced accuracy of 0.825, and a fair balance between sensitivity (90%) and specificity (74%). These models consistently identified a set of psychological traits (optimism, perceived ability to cope with trauma, resilience as trait, ability to comprehend the illness), and subjective perceptions of personal functionality (physical, social, cognitive) as key factors accounting for concurrent depression symptoms. A comprehensive supervised learning pipeline is proposed for the identification of predictors of depression symptoms which could severely impede adaptation to illness.
AB - Displaying resilience following a diagnosis of breast cancer is crucial for successful adaptation to illness, well-being, and health outcomes. Several theoretical and computational models have been proposed toward understanding the complex process of illness adaptation, involving a large variety of patient sociodemographic, lifestyle, medical, and psychological characteristics. To date, conventional multivariate statistical methods have been used extensively to model resilience. In the present work we describe a computational pipeline designed to identify the most prominent predictors of mental health outcomes following breast cancer diagnosis. A machine learning framework was developed and tested on the baseline data (recorded immediately post diagnosis) from an ongoing prospective, multinational study. This fully annotated dataset includes socio-demographic, lifestyle, medical and self-reported psychological characteristics of women recently diagnosed with breast cancer (N = 609). Nine different feature selection and cross-validated classification schemes were compared on their performance in classifying patients into low vs high depression symptom severity. Best-performing approaches involved a meta-estimator combined with a Support Vector Machines (SVMs) classification algorithm, exhibiting balanced accuracy of 0.825, and a fair balance between sensitivity (90%) and specificity (74%). These models consistently identified a set of psychological traits (optimism, perceived ability to cope with trauma, resilience as trait, ability to comprehend the illness), and subjective perceptions of personal functionality (physical, social, cognitive) as key factors accounting for concurrent depression symptoms. A comprehensive supervised learning pipeline is proposed for the identification of predictors of depression symptoms which could severely impede adaptation to illness.
KW - Breast cancer
KW - Classification
KW - Depression
KW - Machine learning
KW - Mental health outcomes
KW - Resilience effects
UR - http://www.scopus.com/inward/record.url?scp=85101017964&partnerID=8YFLogxK
U2 - 10.1016/j.compbiomed.2021.104266
DO - 10.1016/j.compbiomed.2021.104266
M3 - Article
C2 - 33607379
AN - SCOPUS:85101017964
SN - 0010-4825
VL - 131
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 104266
ER -