PURPOSE The integration of patient-reported outcomes (PROs) into electronic health
records (EHRs) has enabled systematic collection of symptom data to manage post …

Health related quality of life (HRQoL) is increasingly assessed in oncology research and
routine care, which has led to the inclusion of HRQoL in prediction models. This review aims …

Background Different curative treatment modalities need to be considered in case of
localized prostate cancer, all comparable in terms of survival and recurrence though …

Introduction Urinary incontinence (UI) is a common side effect of prostate cancer treatment,
but in clinical practice, it is difficult to predict. Machine learning (ML) models have shown …

Purpose This study aims to develop and externally validate a clinically plausible Bayesian
network structure to predict one-year erectile dysfunction in prostate cancer patients by …

Objectives: While AI has the potential to transform cancer care, there has been limited
progress in incorporating AI tools into clinical practice. As healthcare providers work towards …

Objectives: To develop a machine learning-based model for the identification of features
involved in the prediction of long-term erectile dysfunction (ED) after COVID-19 recovery …