tetano
Editor, Senior Moderator
BMC Infect Dis
. 2024 May 2;24(1):466.
doi: 10.1186/s12879-024-09358-1. Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study
Younghee Cho[SUP] 1 2 [/SUP], Hyang Kyu Lee[SUP] 1 [/SUP], Joungyoun Kim[SUP] 3 [/SUP], Ki-Bong Yoo[SUP] 4 [/SUP], Jongrim Choi[SUP] 5 [/SUP], Yongseok Lee[SUP] 2 [/SUP], Mona Choi[SUP] 6 7 [/SUP]
Affiliations
Background: Hospital-acquired influenza (HAI) is under-recognized despite its high morbidity and poor health outcomes. The early detection of HAI is crucial for curbing its transmission in hospital settings.
Aim: This study aimed to investigate factors related to HAI, develop predictive models, and subsequently compare them to identify the best performing machine learning algorithm for predicting the occurrence of HAI.
Methods: This retrospective observational study was conducted in 2022 and included 111 HAI and 73,748 non-HAI patients from the 2011-2012 and 2019-2020 influenza seasons. General characteristics, comorbidities, vital signs, laboratory and chest X-ray results, and room information within the electronic medical record were analysed. Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN) techniques were used to construct the predictive models. Employing randomized allocation, 80% of the dataset constituted the training set, and the remaining 20% comprised the test set. The performance of the developed models was assessed using metrics such as the area under the receiver operating characteristic curve (AUC), the count of false negatives (FN), and the determination of feature importance.
Results: Patients with HAI demonstrated notable differences in general characteristics, comorbidities, vital signs, laboratory findings, chest X-ray result, and room status compared to non-HAI patients. Among the developed models, the RF model demonstrated the best performance taking into account both the AUC (83.3%) and the occurrence of FN (four). The most influential factors for prediction were staying in double rooms, followed by vital signs and laboratory results.
Conclusion: This study revealed the characteristics of patients with HAI and emphasized the role of ventilation in reducing influenza incidence. These findings can aid hospitals in devising infection prevention strategies, and the application of machine learning-based predictive models especially RF can enable early intervention to mitigate the spread of influenza in healthcare settings.
Keywords: Cross infection; Influenza, Human; Logistic models; Machine learning; Patient’s rooms; Random forest.
. 2024 May 2;24(1):466.
doi: 10.1186/s12879-024-09358-1. Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study
Younghee Cho[SUP] 1 2 [/SUP], Hyang Kyu Lee[SUP] 1 [/SUP], Joungyoun Kim[SUP] 3 [/SUP], Ki-Bong Yoo[SUP] 4 [/SUP], Jongrim Choi[SUP] 5 [/SUP], Yongseok Lee[SUP] 2 [/SUP], Mona Choi[SUP] 6 7 [/SUP]
Affiliations
- PMID: 38698304
- DOI: 10.1186/s12879-024-09358-1
Background: Hospital-acquired influenza (HAI) is under-recognized despite its high morbidity and poor health outcomes. The early detection of HAI is crucial for curbing its transmission in hospital settings.
Aim: This study aimed to investigate factors related to HAI, develop predictive models, and subsequently compare them to identify the best performing machine learning algorithm for predicting the occurrence of HAI.
Methods: This retrospective observational study was conducted in 2022 and included 111 HAI and 73,748 non-HAI patients from the 2011-2012 and 2019-2020 influenza seasons. General characteristics, comorbidities, vital signs, laboratory and chest X-ray results, and room information within the electronic medical record were analysed. Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN) techniques were used to construct the predictive models. Employing randomized allocation, 80% of the dataset constituted the training set, and the remaining 20% comprised the test set. The performance of the developed models was assessed using metrics such as the area under the receiver operating characteristic curve (AUC), the count of false negatives (FN), and the determination of feature importance.
Results: Patients with HAI demonstrated notable differences in general characteristics, comorbidities, vital signs, laboratory findings, chest X-ray result, and room status compared to non-HAI patients. Among the developed models, the RF model demonstrated the best performance taking into account both the AUC (83.3%) and the occurrence of FN (four). The most influential factors for prediction were staying in double rooms, followed by vital signs and laboratory results.
Conclusion: This study revealed the characteristics of patients with HAI and emphasized the role of ventilation in reducing influenza incidence. These findings can aid hospitals in devising infection prevention strategies, and the application of machine learning-based predictive models especially RF can enable early intervention to mitigate the spread of influenza in healthcare settings.
Keywords: Cross infection; Influenza, Human; Logistic models; Machine learning; Patient’s rooms; Random forest.