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Beyond Science: How To Empower Women With Breast Cancer

by Karatzanis | Feb 10, 2020 | Issue 2, Newsletters

Empowering women battling with breast cancer is widely recognized as a crucial step toward a successful recovery. Emphasis on empowerment represents a growing trend worldwide aspiring to place the patient at the center of health care. Amidst various definitions of...

Engaging Stakeholders For A More Sustainable Output

by Karatzanis | Feb 10, 2020 | Issue 2, Newsletters

One of the principal goals of the BOUNCE project is to develop a decision support system that can predict the resilience trajectories of breast cancer patients. Understanding stakeholders’ needs is crucial for the BOUNCE consortium – without that it would be...

Psychosocial Profiles Women Undergoing Treatment For Breast Cancer Through Adaptive Algorithms

by Karatzanis | Feb 10, 2020 | Issue 2, Newsletters

The ultimate goal of the BOUNCE project is to provide adaptive decision-support algorithms and a digital tool to measure resilience of early breast cancer patients throughout oncological treatments and during the rehabilitation period. Optimally this would help...

The BOUNCE Project at a glance: Meeting The Challenge Of A Multinational Prospective Study On Resilience

by Karatzanis | Feb 10, 2020 | Issue 2, Newsletters

The ultimate goal of the BOUNCE project is to provide adaptive decision-support algorithms and a digital tool to measure resilience of early breast cancer patients throughout oncological treatments and during the rehabilitation period. Optimally this would help...

Latest Highlights

  • In-Silico Medicine: a new emergent domain embedded in BOUNCE project for supporting breast cancer patients
  • Depression and Anxiety in patients with breast cancer: The impact of the Covid-19 pandemic
  • “CHEMOBRAIN” How to deal with the cognitive changes during and after treatments?
  • Transferring the BOUNCE predictive model into clinical practice: The IEO intervention testing pilot
  • The BOUNCE Decision Support Tool: Linking Machine Learning Risk Assessment to Individualized Clinical Recommendations
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