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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

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P-00T-HE0

Author(s)

Kourou, K

Manikis, G

Poikonen Saksela, P

Mazzocco, K

Pat Horenczyk, R

Sousa, B

Oliveira Maia, AJ

Mattson, J

Roziner, I

Pettini, G

Kondylakis, H

Marias, K

Karademas, E

Simos, P

Fotiadis, DI

Tipo de Documento

Article

Year published

2021

Publicado

in COMPUTERS IN BIOLOGY AND MEDICINE, ISSN: 0010-4825

Volume: 131, Páginas: 104266 (11)

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Publication Identifiers

DOI: 10.1016/j.compbiomed.2021.104266

Pubmed: 33607379

SCOPUS: 2-s2.0-85101017964

Wos: WOS:000631650700001

Source Identifiers

ISSN: 0010-4825

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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

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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

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