A machine learning-based pipeline for modeling medical, socio-demographic, lifestyle and self-report...
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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Author / Creator
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 and Fotiadis, Dimitrios I
Publisher
United States: Elsevier Ltd
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Language
English
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Publisher
United States: Elsevier Ltd
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Scope and Contents
Contents
AbstractDisplaying 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, medic...
Alternative Titles
Full title
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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TN_cdi_proquest_miscellaneous_2491946188
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_2491946188
Other Identifiers
ISSN
0010-4825
E-ISSN
1879-0534
DOI
10.1016/j.compbiomed.2021.104266
