Predictive genetic panel for adult asthma using machine learning methods.

Background: Asthma is a chronic inflammatory disease of the airways that is heterogeneous and multifactorial, making its accurate characterization a complex process. Therefore, identifying the genetic variations associated with asthma and discovering the molecular interactions between the omics that confer risk of developing this disease will help us to unravel the biological pathways involved in its pathogenesis.

Objective: We sought to develop a predictive genetic panel for asthma using machine learning methods.

Bridging Learning in Medicine and Citizenship During the COVID-19 Pandemic: A Telehealth-Based Case Study.

Background: COVID-19 presented great challenges for not only those in the field of health care but also those undergoing medical training. The burden on health care services worldwide has limited the educational opportunities available for medical students due to social distancing requirements.

Objective: In this paper, we describe a strategy that combines telehealth and medical training to mitigate the adverse effects of the COVID-19 pandemic.