This is the fifth episode of the ACVC Talks on Digital Health series.
Recent years have witnessed an increasing amount of available electronic health record (EHR) data and machine learning (ML) techniques have been evolving considerably. Managing and modeling this amount of information may lead to several challenges, such as sparse annotations over time and model interpretability. Starting from these motivations, novel ML methods can be designed in order to overcome these challenges. The ML solutions can also be integrated into a Clinical Decision Support system.
In this video, we will learn how EHRs should be designed and implemented, as well as how machine learning techniques can gather information from routine care.
Presented by Luca Romeo, Assistant Professor in Computer Science, Department Economics and Law, University of Macerata, Italy.