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
Ehsan Nabovati; Amir Abas Azizi; Ebrahim Abbasi; Hassan Vakili-Arki; Javad Zarei; Amir Reza Razavi
Volume 10, Issue 6 , December 2012, , Pages 789-799
Abstract
Introduction: In the past decades, machine learning algorithms have become a useful tool for data mining within huge amounts of health data to create prediction models. Burn is one of the diseases that predicting of its outcome has high importance. The aim of this study was to survey two widely used ...
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Introduction: In the past decades, machine learning algorithms have become a useful tool for data mining within huge amounts of health data to create prediction models. Burn is one of the diseases that predicting of its outcome has high importance. The aim of this study was to survey two widely used machine learning algorithms; neural network and decision tree, and compare them with logistic regression method to predict the outcome of burn patients. Methods: In this retrospective observational study, following preprocessing of the data and determining the outcome of patient (live or death), two well-known machine learning algorithms (neural network and decision tree) and logistic regression method were used to create prediction models using data from 4804 burn patients hospitalized in Taleghani Burn Center in Ahvaz during the years 2001-2007. The preprocessing of the data was performed using SPSS (Version16.0), and in the modeling phase, Clementine (Version 12.0) software was used. Moreover, 10-fold cross validation technique was used to validate the model and criteria for evaluating the performance of models were measured and compared. Results: The results showed that the neural network algorithm with accuracy of 97% resulted the most accurate model on the studied data. The decision tree model with 95% accuracy was in the second place and the logistic regression model with an accuracy of 90% was the least accurate. Moreover other evaluating criteria such as sensitivity, specificity, PPV, NPV and AUC showed that performance of the neural network model was better than the others. Conclusion: The current study shows that machine learning algorithms compared with statistical methods create more accurate models. In analyzing the current data, the model created by artificial neural network is more accurate than the other machine learning algorithm, decision tree. Keywords: Data Mining; Machine Learning; Forecasting; Decision Tree; Artificial Neural Network; Burns
