Predicting Fear of Breast Cancer Recurrence in women five years after diagnosis using Machine Learning and healthcare reimbursement data from the French nationwide VICAN survey.

Objective: A major concern for cancer survivors after treatment is the Fear of Cancer Recurrence (FCR), which is the fear that cancer will reappear or progress. This fear can be exacerbated by medical uncertainty about the future, leading to harmful obsession and having a negative impact on quality of life. This study aims to develop a predictive Machine Learning (ML) model using healthcare reimbursement data to better predict FCR and understand the factors influencing FCR in women with breast cancer five years after their diagnosis.

Proof of concept study on early forecasting of antimicrobial resistance in hospitalized patients using machine learning and simple bacterial ecology data.

Antibiotic resistance in bacterial pathogens is a major threat to global health, exacerbated by the misuse of antibiotics. In hospital practice, results of bacterial cultures and antibiograms can take several days. Meanwhile, prescribing an empirical antimicrobial treatment is challenging, as clinicians must balance the antibiotic spectrum against the expected probability of susceptibility.

Implementation and outcomes of Hugo(TM) RAS System in robotic-assisted radical prostatectomy.

Background: The results and benefits of Robotic-assisted Radical Prostatectomy (RARP) are already established in the literature. However, new robotic platforms have been released recently in the market and their outcomes are still unknown. In this scenario, our objective is to describe our experience implementing the HugoTM RAS robot and report the clinical data of patients who underwent Robotic-assisted Radical Prostatectomy.