TY - GEN
T1 - Investigating patients’ visits to emergency departments
T2 - 7th International Conference on HCI in Business, Government, and Organizations, HCIBGO 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020
AU - Feng, Yen Yi
AU - Wu, I. Chin
AU - Ho, Yu Ping
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Increasing healthcare costs have motivated researchers to seek ways to more efficiently use medical resources. The aim of our study was to adopt the explanatory data-mining approach to identify characteristics of emergency department (ED) visits for ED management. To that end, we adopted a behavior-based decision tree (DT) induction method that considers medical diagnoses and individual patients’ information, i.e., 11 input variables, in order to analyze characteristics of patients’ visits to EDs and predict the length of the stays. We interpreted the results based on the communicability and consistency of the DT, represented as a behavior-based DT profile in order to increase its explanatory power. Among the major preliminary findings, the DT with International Classification of Diseases diagnosis codes achieved better clinical values for explaining the characteristics of patients’ visits. Our results can serve as a reference for ED personnel to examine overcrowding conditions as part of medical management.
AB - Increasing healthcare costs have motivated researchers to seek ways to more efficiently use medical resources. The aim of our study was to adopt the explanatory data-mining approach to identify characteristics of emergency department (ED) visits for ED management. To that end, we adopted a behavior-based decision tree (DT) induction method that considers medical diagnoses and individual patients’ information, i.e., 11 input variables, in order to analyze characteristics of patients’ visits to EDs and predict the length of the stays. We interpreted the results based on the communicability and consistency of the DT, represented as a behavior-based DT profile in order to increase its explanatory power. Among the major preliminary findings, the DT with International Classification of Diseases diagnosis codes achieved better clinical values for explaining the characteristics of patients’ visits. Our results can serve as a reference for ED personnel to examine overcrowding conditions as part of medical management.
KW - Behavior-based profile
KW - Decision tree
KW - Emergency department
KW - International Classification of Diseases diagnosis codes
UR - http://www.scopus.com/inward/record.url?scp=85088751686&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85088751686&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-50341-3_3
DO - 10.1007/978-3-030-50341-3_3
M3 - Conference contribution
AN - SCOPUS:85088751686
SN - 9783030503406
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 34
EP - 45
BT - HCI in Business, Government and Organizations - 7th International Conference, HCIBGO 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings
A2 - Nah, Fiona Fui-Hoon
A2 - Siau, Keng
PB - Springer
Y2 - 19 July 2020 through 24 July 2020
ER -