TY - GEN
T1 - Predicting Hospital Admission by Adding Chief Complaints Using Machine Learning Approach
AU - Wu, I. Chin
AU - Chen, Chu En
AU - Lin, Zhi Rou
AU - Chen, Tzu Li
AU - Feng, Yen Yi
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Overcrowded conditions in emergency departments (EDs) have increased patients’ waiting time, while the variety of patient afflictions has caused difficulties in the allocation of medical resources. Therefore, the ability to predict a patient’s hospital admission at the time of triage could allocate medical resources to patients who go to EDs in urgent need of immediate care. Using a dataset from the MacKay Memorial Hospital in Taipei (Taiwan), which contains 177,038 valid records collected from 2009 to 2010 in this research, we aim to have on hand chief complaints (CCs), demographic data, administration information and clinical information at the triage stage to predict the probability of a patient’s hospital admission. Firstly, we select terms from the CCs to predict which patients may require eventual hospitalization. We then integrate the selected terms with several algorithms to predict the probability of patient admissions. Accordingly, this research includes a series of machine learning processes, such as data preprocessing for structure data and CC data, imbalanced data processing, models construction by logic regression, neural networks, random forest, XGBoost, and model evaluation. The research results show that the ensemble learning approach, XGBoost, can achieve 0.88, and 0.76 in terms of accuracy and AUC respectively. The results show that triage, fever status, age, and terms extracted from the CCs are important attributes to predict if patients should be hospitalized. The results of this study will provide a reference approach in the field of emergency hospital admissions prediction and help hospitals improve resource allocation in emergency rooms.
AB - Overcrowded conditions in emergency departments (EDs) have increased patients’ waiting time, while the variety of patient afflictions has caused difficulties in the allocation of medical resources. Therefore, the ability to predict a patient’s hospital admission at the time of triage could allocate medical resources to patients who go to EDs in urgent need of immediate care. Using a dataset from the MacKay Memorial Hospital in Taipei (Taiwan), which contains 177,038 valid records collected from 2009 to 2010 in this research, we aim to have on hand chief complaints (CCs), demographic data, administration information and clinical information at the triage stage to predict the probability of a patient’s hospital admission. Firstly, we select terms from the CCs to predict which patients may require eventual hospitalization. We then integrate the selected terms with several algorithms to predict the probability of patient admissions. Accordingly, this research includes a series of machine learning processes, such as data preprocessing for structure data and CC data, imbalanced data processing, models construction by logic regression, neural networks, random forest, XGBoost, and model evaluation. The research results show that the ensemble learning approach, XGBoost, can achieve 0.88, and 0.76 in terms of accuracy and AUC respectively. The results show that triage, fever status, age, and terms extracted from the CCs are important attributes to predict if patients should be hospitalized. The results of this study will provide a reference approach in the field of emergency hospital admissions prediction and help hospitals improve resource allocation in emergency rooms.
KW - Chief complaint
KW - Prediction of hospital admission
KW - Triage
KW - XGBoost
UR - http://www.scopus.com/inward/record.url?scp=85133243599&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85133243599&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-05544-7_18
DO - 10.1007/978-3-031-05544-7_18
M3 - Conference contribution
AN - SCOPUS:85133243599
SN - 9783031055430
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 233
EP - 244
BT - HCI in Business, Government and Organizations - 9th International Conference, HCIBGO 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Proceedings
A2 - Fui-Hoon Nah, Fiona
A2 - Siau, Keng
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th International Conference on HCI in Business, Government and Organizations, HCIBGO 2022 Held as Part of the 24th HCI International Conference, HCII 2022
Y2 - 26 June 2022 through 1 July 2022
ER -