Sessions

We present a study comparing two mediums that can be used to communicate with allophone patients: a speech-enabled phraselator (BabelDr) and telephone interpreting. 

Review of the potential of using machine learning for the integration of medical data.

This workshop is designed to provide a comprehensive overview of the EFMI accreditation process, its benefits and importance, key requirements, and criteria, and tips for a successful accreditation application. Aimed at universities and institutions looking to attain EFMI accreditation, this workshop will provide valuable insights and guidance. Our experienced speakers will share their expertise, personal experiences with their master’s accreditation, and answer any questions participants may have about the process.

Patients' remote monitoring platforms can be enhanced with intelligent recommendations and gamification functionalities to support their adherence to care plans. The current paper aims to present a methodology for creating personalized recommendations, which can be used to improve patient remote monitoring and care platforms. The current pilot system design is aimed to support patients by providing recommendations for Sleep, Physical Activity, BMI, Blood sugar, Mental Health, Heart Health, and Chronic Obstructive Pulmonary Disease aspects. The users, through the application, can select the types of recommendations they are interested in. Thus, personalized recommendations based on data obtained by the patients’ records anticipated to be a valuable and a safe approach for patient coaching. The paper discusses the main technical details and provides some initial results.