Tutorial on Argumentation Technology in Medicine
In medicine, complex decisions are made by clinicians often in uncertain conditions. The field of argumentation provides a formal framework for modeling human collaborative deliberations, interchanging arguments in favor or against some conclusion based on incomplete or inconsistent information. Argumentation theory has become an important research field in Artificial Intelligence (AI). The relationship between computer science and the area of philosophy focused on arguments has led to the emergence of a new interdisciplinary field called computational dialectics, argumentation technology, or argument-based computing.
Argumentation has been investigated as a tool for providing clinical decision support, changing health-related behaviors, tailoring explanations, advising patients on treatment regimes, as well as for designing agents working in cooperation within healthcare teams.
In this tutorial, we provide an introduction into argumentation technology for medicine as well as an overview of the main techniques, use cases and applications thereof. We focus in particular on two main applications. First, we discuss how argumentation technology can be used as a basis to generate hypotheses and explanations for anomalous patient responses. Second, we discuss how arguments can be used to support evidence-based decision-making by using arguments as a tool to aggregate evidence across multiple clinical studies.
Agenda June 26, 2019
|14:00 - 14:30||Introduction to argumentation (Philipp Cimiano/Olivia Sanchez)|
|14:30 - 15:00||Argumentation theory in the medical context (Olivia Sanchez)|
|15:00 - 15:45||Argumentation technology for explaining medical hypotheses and anomalous patient responses to treatments (Laura Moss)|
|15:45 - 16:15||Coffee Break|
|16:15 - 16:45||On the need of aggregating evidence across multiple clinical studies (Chistian Witte)|
|16:45 - 17:30||Aggregating evidence using argumentation (Olivia Sanchez)|
|17:30 - 18:00||Framework for rationalising clinical recommendations (Olivia Sanchez)|