Volume 14, Issue 6 (Feb & Mar 2021)
payavard 2021, 14(6): 497-505 |
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Ethics code: IR.TUMS.SPH.REC.1399.073
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Mohammadzadeh N, mosayebi Z, Beigy H, Shojaeinia M. Prediction of Sepsis Due to Acinetobacter Infection in Neonates Admitted to NICU. payavard 2021; 14 (6) :497-505
URL: http://payavard.tums.ac.ir/article-1-7011-en.html
URL: http://payavard.tums.ac.ir/article-1-7011-en.html
1- ssistant Professor, Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
2- Professor, Department of Pediatrics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
3- Associate Professor, Department of Artificial Intelligence, Faculty of Computer Engineering, Sharif University of Technology, Tehran, Iran
4- Master of Sciences in Medical Informatics, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran ,mohashoj2004@gmail.com
2- Professor, Department of Pediatrics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
3- Associate Professor, Department of Artificial Intelligence, Faculty of Computer Engineering, Sharif University of Technology, Tehran, Iran
4- Master of Sciences in Medical Informatics, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran ,
Abstract: (1806 Views)
Background and Aim: Sepsis is the most important disease in the first 28 days of life and one of the main causes of infant mortality in the intensive care unit. Its definitive diagnosis is possible by performing blood culture. Neonatal sepsis can be a clinical sign of nosocomial infections that are often resistant to antibiotics. Therefore, the purpose of this study was to create and evaluate a hospital sepsis prediction model and present its results to health care providers.
Materials and Methods: In this descriptive-applied study, the research population includes neonates admitted to the intensive care unit of Valiasr Hospital in Tehran and the research sample is the data of 4196 neonates admitted to this ward from 2016 to August, 2020. The initial features for creating a predictive model of sepsis were prepared by examining the relevant information sources and under the supervision of professors and officials of Valiasr Hospital's mother and fetus research center and its validity was confirmed by 5 neonatal professors of this hospital. In this research, machine learning algorithms have been used to create a sepsis prediction model.
Results: Accuracy and AUROC(area under the ROC curve) parameters were used to evaluate the generated models. The highest values of Accuracy and AUROC are related to Adaptive Boosting and random forest algorithms, respectively.
Conclusion: Learning curves show that using different training examples and more complex selection of combination features improves the performance of the models. Further research is needed to evaluate the clinical effectiveness of machine learning models in a trial.
Materials and Methods: In this descriptive-applied study, the research population includes neonates admitted to the intensive care unit of Valiasr Hospital in Tehran and the research sample is the data of 4196 neonates admitted to this ward from 2016 to August, 2020. The initial features for creating a predictive model of sepsis were prepared by examining the relevant information sources and under the supervision of professors and officials of Valiasr Hospital's mother and fetus research center and its validity was confirmed by 5 neonatal professors of this hospital. In this research, machine learning algorithms have been used to create a sepsis prediction model.
Results: Accuracy and AUROC(area under the ROC curve) parameters were used to evaluate the generated models. The highest values of Accuracy and AUROC are related to Adaptive Boosting and random forest algorithms, respectively.
Conclusion: Learning curves show that using different training examples and more complex selection of combination features improves the performance of the models. Further research is needed to evaluate the clinical effectiveness of machine learning models in a trial.
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