Health Information management
Saba Paydar; GholamAli Reisi Ardali; Hossein Raeisi
Abstract
AbstractIntroduction: Nowadays, hospitals face challenges such as overcrowding in the emergency department and increased chaos and disruption in staff work, which leads to increased patient dissatisfaction. With the advancement of artificial intelligence and the expansion of data mining, predicting patient ...
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AbstractIntroduction: Nowadays, hospitals face challenges such as overcrowding in the emergency department and increased chaos and disruption in staff work, which leads to increased patient dissatisfaction. With the advancement of artificial intelligence and the expansion of data mining, predicting patient admission has become very important. The aim of this research is to predict patient admission in the emergency department of Imam Ali (AS) Hospital in Shahrekord.Methods: In this research, 2180 patient records from the emergency department of the hospital were examined. Initial patient information, including personal details, vital signs, and triage level which were recorded by nurses, were extracted. Using pairwise comparison matrix, the effective features were selected by experts. Then, using naive Bayes, decision tree, random forest, and support vector machine algorithms, the data was classified. Results: Out of the 15 collected features, 9 features were selected by experts, and the results showed that the random forest algorithm had the best performance in predicting patient admission in this case study, with an accuracy of 92/2%Conclusion: These results demonstrate the importance of using artificial intelligence and data mining methods in hospital management and patient admission prediction. It can serve as a helpful tool in decision-making processes. Key words: Data mining, Forecasting, Patient admission, Hospital Emergency Services
