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Updated: Jun 26, 2025
A Neonatal Imaging Model of Gram-Negative Bacterial Sepsis
Published on: August 12, 2020
Establishment and Verification of an Artificial Intelligence Prediction Model for Children With Sepsis
Li Wang1, Yu-Hui Wu2, Yong Ren3,4,5
1From the Pediatric Intensive Care Unit, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangdong, China.
Insights
Artificial intelligence (AI) effectively predicts mortality risk in pediatric intensive care unit (PICU) sepsis patients. An artificial neural network model demonstrated superior accuracy compared to other machine learning approaches for identifying high-risk children.
Area of Science:
- Pediatric critical care medicine
- Computational biology
- Medical artificial intelligence
Background:
- Sepsis mortality in children necessitates early identification of high-risk individuals.
- Artificial intelligence (AI) offers potential for rapid and accurate sepsis risk prediction in pediatric intensive care units (PICUs).
Purpose of the Study:
- To evaluate the efficacy of AI models in predicting mortality risk among children with sepsis in the PICU.
- To compare the performance of various machine learning (ML) models for sepsis mortality prediction.
Main Methods:
- A retrospective observational study involving 671 pediatric sepsis patients from two hospitals (December 2016 - July 2020).
- Development and comparison of multiple ML models, including artificial neural networks, support vector machines, logical regression, Bayesian, K nearest neighbor, and decision trees.
- Models were trained and tested to predict the risk of death in sepsis patients.
Main Results:
- The artificial neural network (ANN) model achieved the highest accuracy, with 0.99 on the training set and 0.96 on the test set.
- The ANN model outperformed other evaluated ML models (support vector machine, logical regression, Bayesian, K nearest neighbor, decision tree) in predicting sepsis-related mortality.
- Study included 671 children diagnosed with sepsis.
Conclusions:
- AI models, particularly artificial neural networks, are effective tools for predicting sepsis-related mortality risk in PICU settings.
- The superior performance of the ANN model suggests its potential for clinical application in early risk stratification of pediatric sepsis patients.
Background:
Early identification of high-risk groups of children with sepsis is beneficial to reduce sepsis mortality. This article used artificial intelligence (AI) technology to predict the risk of death effectively and quickly in children with sepsis in the pediatric intensive care unit (PICU).
Study Design:
This retrospective observational study was conducted in the PICUs of the First Affiliated Hospital of Sun Yat-sen University from December 2016 to June 2019 and Shenzhen Children's Hospital from January 2019 to July 2020. The children were divided into a death group and a survival group. Different machine language (ML) models were used to predict the risk of death in children with sepsis.
Results:
A total of 671 children with sepsis were enrolled. The accuracy (ACC) of the artificial neural network model was better than that of support vector machine, logical regression analysis, Bayesian, K nearest neighbor method and decision tree models, with a training set ACC of 0.99 and a test set ACC of 0.96.
Conclusions:
The AI model can be used to predict the risk of death due to sepsis in children in the PICU, and the artificial neural network model is better than other AI models in predicting mortality risk.
