A Machine Learning-Based Prediction of Hospital Mortality in Patients With Postoperative Sepsis

Yao, Ren-qi; Jin, Xin; Wang, Guowei; Yu, Yue; Wu, Guosheng; Zhu, Yibing; Li, Lin; Li, Yuxuan; Zhao, Peng-yue; Zhu, Shengyu; Xia, Zhaofan; Ren, Chao; Yao, Yong‐Ming

doi:10.3389/fmed.2020.00445

Cited by 51 publications

(49 citation statements)

References 41 publications

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“…With the development of machine learning algorithms, the magnitude of predictors that can be processed has mainly been largely enriched. Thus, advanced machine learning techniques allow researchers to establish more optimal models in comparison with conventional models ( 16 ). With such models, ICU physicians could be alerted early when patients become complicated and have deteriorated with mechanical ventilation.…”

Section: Discussionmentioning

confidence: 99%

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Machine Learning Prediction Models for Mechanically Ventilated Patients: Analyses of the MIMIC-III Database

et al. 2021

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Background: Mechanically ventilated patients in the intensive care unit (ICU) have high mortality rates. There are multiple prediction scores, such as the Simplified Acute Physiology Score II (SAPS II), Oxford Acute Severity of Illness Score (OASIS), and Sequential Organ Failure Assessment (SOFA), widely used in the general ICU population. We aimed to establish prediction scores on mechanically ventilated patients with the combination of these disease severity scores and other features available on the first day of admission.Methods: A retrospective administrative database study from the Medical Information Mart for Intensive Care (MIMIC-III) database was conducted. The exposures of interest consisted of the demographics, pre-ICU comorbidity, ICU diagnosis, disease severity scores, vital signs, and laboratory test results on the first day of ICU admission. Hospital mortality was used as the outcome. We used the machine learning methods of k-nearest neighbors (KNN), logistic regression, bagging, decision tree, random forest, Extreme Gradient Boosting (XGBoost), and neural network for model establishment. A sample of 70% of the cohort was used for the training set; the remaining 30% was applied for testing. Areas under the receiver operating characteristic curves (AUCs) and calibration plots would be constructed for the evaluation and comparison of the models' performance. The significance of the risk factors was identified through models and the top factors were reported.Results: A total of 28,530 subjects were enrolled through the screening of the MIMIC-III database. After data preprocessing, 25,659 adult patients with 66 predictors were included in the model analyses. With the training set, the models of KNN, logistic regression, decision tree, random forest, neural network, bagging, and XGBoost were established and the testing set obtained AUCs of 0.806, 0.818, 0.743, 0.819, 0.780, 0.803, and 0.821, respectively. The calibration curves of all the models, except for the neural network, performed well. The XGBoost model performed best among the seven models. The top five predictors were age, respiratory dysfunction, SAPS II score, maximum hemoglobin, and minimum lactate.Conclusion: The current study indicates that models with the risk of factors on the first day could be successfully established for predicting mortality in ventilated patients. The XGBoost model performs best among the seven machine learning models.

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Section: Discussionmentioning

confidence: 99%

“…A previous study conducted by Yao et al ( 16 ) explored the death prediction model in postoperative septic patients using the MIMIC-III database. Similar to our results, they also found that the XGBoost model performed better in predicting hospital mortality than the other models.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Prediction Models for Mechanically Ventilated Patients: Analyses of the MIMIC-III Database

et al. 2021

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“…As mentioned above, sepsis prediction on the ICU is an important and timely problem and an active area of research. Under these circumstances, it is not surprising that some approaches have been developed in parallel to this work [33][34][35][36][37][38][39][40][41][42][43]. It will be an exciting and important avenue for future work to benchmark all these approaches (including ours) against each other and to compare their performances on a unified and realistic set of sepsis labels, for instance, the ones we propose in this work.…”

Section: Parallel Work On Sepsis Predictionmentioning

confidence: 97%

MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis

Rosnati

Fortuin

2021

PLoS ONE

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With a mortality rate of 5.4 million lives worldwide every year and a healthcare cost of more than 16 billion dollars in the USA alone, sepsis is one of the leading causes of hospital mortality and an increasing concern in the ageing western world. Recently, medical and technological advances have helped re-define the illness criteria of this disease, which is otherwise poorly understood by the medical society. Together with the rise of widely accessible Electronic Health Records, the advances in data mining and complex nonlinear algorithms are a promising avenue for the early detection of sepsis. This work contributes to the research effort in the field of automated sepsis detection with an open-access labelling of the medical MIMIC-III data set. Moreover, we propose MGP-AttTCN: a joint multitask Gaussian Process and attention-based deep learning model to early predict the occurrence of sepsis in an interpretable manner. We show that our model outperforms the current state-of-the-art and present evidence that different labelling heuristics lead to discrepancies in task difficulty. For instance, when predicting sepsis five hours prior to onset on our new realistic labels, our proposed model achieves an area under the ROC curve of 0.660 and an area under the PR curve of 0.483, whereas the (less interpretable) previous state-of-the-art model (MGP-TCN) achieves 0.635 AUROC and 0.460 AUPR and the popular commercial InSight model achieves 0.490 AUROC and 0.359 AUPR.

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“…For example, Fohner et al used latent Dirichlet Allocation as the un-supervised learning model to assess clinical heterogeneity in sepsis, and Taylor et al applied the random forest model to predict the in-hospital mortality in emergency department patients with sepsis (Taylor et al, 2016;Fohner et al, 2019). Extreme Gradient Boosting (Xgboost), as it functions as an iterative refit of weak classifiers to residuals of previous models (Yao et al, 2020), has become one of the most popular machine learning models, outperforming other models. It has been widely used in different scenarios in medical application Ogunleye and Wang, 2020), and there is no exception for sepsis (Zabihi et al, 2019;Yao et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Extreme Gradient Boosting (Xgboost), as it functions as an iterative refit of weak classifiers to residuals of previous models (Yao et al, 2020), has become one of the most popular machine learning models, outperforming other models. It has been widely used in different scenarios in medical application Ogunleye and Wang, 2020), and there is no exception for sepsis (Zabihi et al, 2019;Yao et al, 2020). To our knowledge, there is no analytical tool to predict which COVID-19 patients are most likely to develop sepsis in the near future.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Sepsis in COVID-19 Using Laboratory Indicators

Tang

Luo

Feng

et al. 2021

Front. Cell. Infect. Microbiol.

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BackgroundThe outbreak of coronavirus disease 2019 (COVID-19) has become a global public health concern. Many inpatients with COVID-19 have shown clinical symptoms related to sepsis, which will aggravate the deterioration of patients’ condition. We aim to diagnose Viral Sepsis Caused by SARS-CoV-2 by analyzing laboratory test data of patients with COVID-19 and establish an early predictive model for sepsis risk among patients with COVID-19.MethodsThis study retrospectively investigated laboratory test data of 2,453 patients with COVID-19 from electronic health records. Extreme gradient boosting (XGBoost) was employed to build four models with different feature subsets of a total of 69 collected indicators. Meanwhile, the explainable Shapley Additive ePlanation (SHAP) method was adopted to interpret predictive results and to analyze the feature importance of risk factors.FindingsThe model for classifying COVID-19 viral sepsis with seven coagulation function indicators achieved the area under the receiver operating characteristic curve (AUC) 0.9213 (95% CI, 89.94–94.31%), sensitivity 97.17% (95% CI, 94.97–98.46%), and specificity 82.05% (95% CI, 77.24–86.06%). The model for identifying COVID-19 coagulation disorders with eight features provided an average of 3.68 (±) 4.60 days in advance for early warning prediction with 0.9298 AUC (95% CI, 86.91–99.04%), 82.22% sensitivity (95% CI, 67.41–91.49%), and 84.00% specificity (95% CI, 63.08–94.75%).InterpretationWe found that an abnormality of the coagulation function was related to the occurrence of sepsis and the other routine laboratory test represented by inflammatory factors had a moderate predictive value on coagulopathy, which indicated that early warning of sepsis in COVID-19 patients could be achieved by our established model to improve the patient’s prognosis and to reduce mortality.

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A Machine Learning-Based Prediction of Hospital Mortality in Patients With Postoperative Sepsis

Cited by 51 publications

References 41 publications

Machine Learning Prediction Models for Mechanically Ventilated Patients: Analyses of the MIMIC-III Database

Machine Learning Prediction Models for Mechanically Ventilated Patients: Analyses of the MIMIC-III Database

MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis

Prediction of Sepsis in COVID-19 Using Laboratory Indicators

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