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Computational Models for Predicting Resilience Levels of Women with Breast Cancer

Part of the IFMBE Proceedings book series (IFMBE,volume 76)

Abstract

In the current study, a model-based system for predicting resilience in silico, as part of personalizing precision medicine, to better understand the needs for improved therapeutic protocols of each patient is proposed. The computational environment, which is currently under implementation within the BOUNCE EU project (“Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back”), will help clinicians and care-givers to predict the patient’s resilience trajectory throughout cancer continuum. The overall proposed system architecture contributes to clinical outcomes and patient well-being by taking into consideration biological, social, environmental, occupational and lifestyle factors for resilience prediction in women with breast cancer. Supervised, semisupervised and unsupervised learning algorithms are adopted with the inherent ability to represent the time-varying behaviour of the underlying system which allows for a better representation of spatiotemporal input-output dependencies. The in silico resilience prediction approach accommodates numerous diverse factors contributing to multi-scale models of cancer, in order to better specify clinically useful aspects of recovery, treatment and intervention.

Keywords

  • Breast cancer
  • Resilience prediction
  • Psychological and behavioural data
  • Machine learning models
  • Model aggregation

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Acknowledgment

This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 777167.

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Correspondence to Dimitrios I. Fotiadis .

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Kourou, K. et al. (2020). Computational Models for Predicting Resilience Levels of Women with Breast Cancer. In: Henriques, J., Neves, N., de Carvalho, P. (eds) XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019. MEDICON 2019. IFMBE Proceedings, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-31635-8_62

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  • DOI: https://doi.org/10.1007/978-3-030-31635-8_62

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