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Randomized Controlled Trial
. 2021 Oct 15:9:754348.
doi: 10.3389/fpubh.2021.754348. eCollection 2021.

A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients

Dong Wang  1   2   3 , Jinbo Li  1   4 , Yali Sun  1   2   3 , Xianfei Ding  1   2   3 , Xiaojuan Zhang  1   2   3 , Shaohua Liu  1   2   3 , Bing Han  1   2   3 , Haixu Wang  1   2   3 , Xiaoguang Duan  1   2   3 , Tongwen Sun  1   2   3
Affiliations
Randomized Controlled Trial

A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients

Dong Wang et al. Front Public Health. .

Abstract

Background: Although numerous studies are conducted every year on how to reduce the fatality rate associated with sepsis, it is still a major challenge faced by patients, clinicians, and medical systems worldwide. Early identification and prediction of patients at risk of sepsis and adverse outcomes associated with sepsis are critical. We aimed to develop an artificial intelligence algorithm that can predict sepsis early. Methods: This was a secondary analysis of an observational cohort study from the Intensive Care Unit of the First Affiliated Hospital of Zhengzhou University. A total of 4,449 infected patients were randomly assigned to the development and validation data set at a ratio of 4:1. After extracting electronic medical record data, a set of 55 features (variables) was calculated and passed to the random forest algorithm to predict the onset of sepsis. Results: The pre-procedure clinical variables were used to build a prediction model from the training data set using the random forest machine learning method; a 5-fold cross-validation was used to evaluate the prediction accuracy of the model. Finally, we tested the model using the validation data set. The area obtained by the model under the receiver operating characteristic (ROC) curve (AUC) was 0.91, the sensitivity was 87%, and the specificity was 89%. Conclusions: This newly established machine learning-based model has shown good predictive ability in Chinese sepsis patients. External validation studies are necessary to confirm the universality of our method in the population and treatment practice.

Keywords: ICU patients; infection; machine learning; prognostication; sepsis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow chart depicting number of patients who were included in analysis after exclusion criteria. The total included encounters were divided into those with and without sepsis.
Figure 2
Figure 2
Importance of the 20 variables included in the predictive model for sepsis events.
Figure 3
Figure 3
The relationship between the cross-validation error and the number of variables.
Figure 4
Figure 4
ROC curve (of the testing set) for predicting Sepsis events using the predictive model. ROC receiver operating characteristic.
See this image and copyright information in PMC

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