Novel Machine Learning HIV Intervention for Sexual and Gender Minority Young People Who Have Sex With Men (uTECH): Protocol for a Randomized Comparison Trial.

Background: Sexual and gender minority (SGM) young people are disproportionately affected by HIV in the United States, and substance use is a major driver of new infections. People who use web-based venues to meet sex partners are more likely to report substance use, sexual risk behaviors, and sexually transmitted infections. To our knowledge, no machine learning (ML) interventions have been developed that use web-based and digital technologies to inform and personalize HIV and substance use prevention efforts for SGM young people.

Post discharge management of heart failure patients: clinical findings at the first medical visit in a single-center study.

Background: The Post Discharge Management of patients with heart failure impact significantly their incomes. This study aims to analyze the clinical findings and management at the first medical visit of these patients in our context.

Material And Methods: This is a retrospective cross-sectional descriptive study on consecutive files of patients hospitalized for heart failure from January to December 2018 in our Department.