TY - GEN
T1 - Automated Extraction of Causal Relations from Text for Teaching Surgical Concepts
AU - Yin, Myat Su
AU - Pomarlan, Mihai
AU - Haddawy, Peter
AU - Tabassam, Muhammad Rauf
AU - Chaimanakarn, Chitpol
AU - Srimaneekarn, Natchalee
AU - Hassan, Saeed Ul
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - 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.
AB - 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.
KW - causal relation
KW - ontology
KW - surgery
KW - text mining
UR - http://www.scopus.com/inward/record.url?scp=85103149962&partnerID=8YFLogxK
U2 - 10.1109/ICHI48887.2020.9374310
DO - 10.1109/ICHI48887.2020.9374310
M3 - Conference contribution
AN - SCOPUS:85103149962
T3 - 2020 IEEE International Conference on Healthcare Informatics, ICHI 2020
BT - 2020 IEEE International Conference on Healthcare Informatics, ICHI 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Healthcare Informatics, ICHI 2020
Y2 - 30 November 2020 through 3 December 2020
ER -