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: An initial effort to define resilience effects. (April 2021)

• Record Type: Journal Article
• Authors: Kourou, Konstantina; Manikis, Georgios; Poikonen-Saksela, Paula; Mazzocco, Ketti; Pat-Horenczyk, Ruth; Sousa, Berta; Oliveira-Maia, Albino J.; Mattson, Johanna; Roziner, Ilan; Pettini, Greta; Kondylakis, Haridimos; Marias, Kostas; Karademas, Evangelos; Simos, Panagiotis; Fotiadis, Dimitrios I.
• Is Part Of: Computers in biology and medicine. Volume 131(2021)
• Publication Date: 2021-04
• Subjects: Breast cancer -- Classification -- Depression -- Machine learning -- Mental health outcomes -- Resilience effects; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104266
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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. Highlights: A comprehensive supervised learning pipeline is proposed for the identification of predictors of depression symptoms which could severely impede adaptation to illness. Nine different feature selection and cross-validated classification schemes were compared on their performance in classifying patients into low vs high depression symptom severity. Standardized scales and questionnaires were used to assess domains of personality, coping, social support, resilience as a personality characteristic (trait), Quality of Life (QoL), distress and psychological symptoms to further stratify patients according to the severity of concurrent depression symptoms.