Predicting Hospital Admission by Adding Chief Complaints Using Machine Learning Approach

I. Chin Wu*, Chu En Chen, Zhi Rou Lin, Tzu Li Chen, Yen Yi Feng

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

1 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
主出版物標題HCI in Business, Government and Organizations - 9th International Conference, HCIBGO 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Proceedings
編輯Fiona Fui-Hoon Nah, Keng Siau
發行者Springer Science and Business Media Deutschland GmbH
頁面233-244
頁數12
ISBN(列印)9783031055430
DOIs
出版狀態已發佈 - 2022
事件9th International Conference on HCI in Business, Government and Organizations, HCIBGO 2022 Held as Part of the 24th HCI International Conference, HCII 2022 - Virtual, Online
持續時間: 2022 6月 262022 7月 1

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
13327 LNCS
ISSN(列印)0302-9743
ISSN(電子)1611-3349

會議

會議9th International Conference on HCI in Business, Government and Organizations, HCIBGO 2022 Held as Part of the 24th HCI International Conference, HCII 2022
城市Virtual, Online
期間2022/06/262022/07/01

ASJC Scopus subject areas

  • 理論電腦科學
  • 一般電腦科學

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