Handling concept drift in medical applications: Importance, challenges and solutions

In the real world data is often non stationary. In supervised learning, concept drift means that the statistical properties of the target variable, which the model aims to predict, change over time unexpectedly. This causes problems because the predictions might become less accurate as the time passes or opportunities to improve the accuracy might be missed. With the proposed tutorial we intend to reach the following goals: 1) highlight the importance of concept drift handling mechanisms in medical applications; 2) overview existing approaches for handling different types of drift in supervised learning, emphasizing the underlying assumptions that these approaches implicitly or explicitly make about the nature and causes of changes; 3) discuss practical aspects of applying drift handling mechanisms to a wide range of medical applications and present a foreseen development in this field.