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Article

Machine Learning Approaches to Predict In-Hospital Mortality among Neonates with Clinically Suspected Sepsis in the Neonatal Intensive Care Unit

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Division of Neonatology, Department of Pediatrics, Linkou Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan
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School of Medicine, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
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Artificial Intelligence Research Center and Molecular Medicine Research Center, Chang Gung University, Taoyuan 33302, Taiwan
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Department of Computer Science and Information Engineering, Providence University, Taichung 433301, Taiwan
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Brain Research Center, National Tsing Hua University, Hsinchu 300044, Taiwan
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Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan
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Division of Neonatology and Pediatric Hematology/Oncology, Department of Pediatrics, Chang Gung Memorial Hospital, Yunlin 61363, Taiwan
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Author to whom correspondence should be addressed.
Academic Editor: Niels Bergsland
J. Pers. Med. 2021, 11(8), 695; https://doi.org/10.3390/jpm11080695
Received: 21 May 2021 / Revised: 12 July 2021 / Accepted: 21 July 2021 / Published: 22 July 2021
Background: preterm and critically ill neonates often experience clinically suspected sepsis during their prolonged hospitalization in the neonatal intensive care unit (NICU), which can be the initial sign of final adverse outcomes. Therefore, we aimed to utilize machine learning approaches to predict neonatal in-hospital mortality through data-driven learning. Methods: a total of 1095 neonates who experienced clinically suspected sepsis in a tertiary-level NICU in Taiwan between August 2017 and July 2020 were enrolled. Clinically suspected sepsis was defined based on clinical features and laboratory criteria and the administration of empiric antibiotics by clinicians. The variables used for analysis included patient demographics, clinical features, laboratory data, and medications. The machine learning methods used included deep neural network (DNN), k-nearest neighbors, support vector machine, random forest, and extreme gradient boost. The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC). Results: the final in-hospital mortality of this cohort was 8.2% (90 neonates died). A total of 765 (69.8%) and 330 (30.2%) patients were randomly assigned to the training and test sets, respectively. Regarding the efficacy of the single model that most accurately predicted the outcome, DNN exhibited the greatest AUC (0.923, 95% confidence interval [CI] 0.953–0.893) and the best accuracy (95.64%, 95% CI 96.76–94.52%), Cohen’s kappa coefficient value (0.74, 95% CI 0.79–0.69) and Matthews correlation coefficient value (0.75, 95% CI 0.80–0.70). The top three most influential variables in the DNN importance matrix plot were the requirement of ventilator support at the onset of suspected sepsis, the feeding conditions, and intravascular volume expansion. The model performance was indistinguishable between the training and test sets. Conclusions: the DNN model was successfully established to predict in-hospital mortality in neonates with clinically suspected sepsis, and the machine learning algorithm is applicable for clinicians to gain insights and have better communication with families in advance. View Full-Text
Keywords: neonatal mortality; artificial intelligence; big data analysis; early prediction; machine learning neonatal mortality; artificial intelligence; big data analysis; early prediction; machine learning
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MDPI and ACS Style

Hsu, J.-F.; Chang, Y.-F.; Cheng, H.-J.; Yang, C.; Lin, C.-Y.; Chu, S.-M.; Huang, H.-R.; Chiang, M.-C.; Wang, H.-C.; Tsai, M.-H. Machine Learning Approaches to Predict In-Hospital Mortality among Neonates with Clinically Suspected Sepsis in the Neonatal Intensive Care Unit. J. Pers. Med. 2021, 11, 695. https://doi.org/10.3390/jpm11080695

AMA Style

Hsu J-F, Chang Y-F, Cheng H-J, Yang C, Lin C-Y, Chu S-M, Huang H-R, Chiang M-C, Wang H-C, Tsai M-H. Machine Learning Approaches to Predict In-Hospital Mortality among Neonates with Clinically Suspected Sepsis in the Neonatal Intensive Care Unit. Journal of Personalized Medicine. 2021; 11(8):695. https://doi.org/10.3390/jpm11080695

Chicago/Turabian Style

Hsu, Jen-Fu, Ying-Feng Chang, Hui-Jun Cheng, Chi Yang, Chun-Yuan Lin, Shih-Ming Chu, Hsuan-Rong Huang, Ming-Chou Chiang, Hsiao-Chin Wang, and Ming-Horng Tsai. 2021. "Machine Learning Approaches to Predict In-Hospital Mortality among Neonates with Clinically Suspected Sepsis in the Neonatal Intensive Care Unit" Journal of Personalized Medicine 11, no. 8: 695. https://doi.org/10.3390/jpm11080695

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