Automated Extraction of Causal Relations from Text for Teaching Surgical Concepts
Myat Su Yin, Mihai Pomarlan, Peter Haddawy, Muhammad Rauf Tabassam, Chitpol Chaimanakarn, Natchalee Srimaneekarn, Saeed‐Ul Hassan · 2020
Effective teaching of surgical decision making requires providing students with a deep understanding of the domain so that they have the ability to make decisions in novel situations. This means providing them with a thorough understanding of causal relations between actions and their possible effects in the context of various states of the patient as well as previous actions. Intelligent tutoring systems to teach surgical decision making thus require such domain knowledge, but there are currently no medical ontologies that encompass it. While it is possible to engineer the needed ontologies by hand, this requires a large effort for every new domain to be covered. In this paper we explore the possibility of automatically extracting causal relations from textbooks on surgery. Specifically, we adapt the spaCy NLP tool for this task and apply it to a collection of fifteen textbooks on endodontic root canal treatment, which is one of the most challenging areas of dental surgery. Since the main purpose is to extract knowledge for teaching, we focus on actions that can lead to surgical mishaps. We evaluate the precision and recall of the extracted relations using a gold standard prepared by a pair of dental surgeons.