An Ensemble Machine Learning Model for the Early Detection of Sepsis from Clinical Data

Fu, Mengsha; Yuan, Jian; Lu, Menglin; Peng, Hong; Mei, Zeng

doi:10.22489/cinc.2019.317

Cited by 13 publications

(7 citation statements)

References 12 publications

(14 reference statements)

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“…(18). Its quite difficult to deal with disease complexity in ICU and imbalance data, therefore, the advanced methods of machine learning presented the new scoring systems for accurate prediction (13) on training as well as on test dataset (5).Moreover, this study also shows the importance of each feature that is having great impact on sepsis. The statistical anlaysis has been used for the purpose of validation of each attribute on the basis of Z-test.…”

Section: Discussionmentioning

confidence: 74%

“…Its quite difficult to deal with disease complexity in ICU and imbalance data, therefore, the advanced methods of machine learning presented the new scoring systems for accurate prediction (13). The another interesting outcome is every model trained on combined dataset Training set A and Training set B as well as on seperate datasets and showing better results on training as well as on test dataset (5). Moreover, this study also shows the importance of each feature that is having great impact on sepsis.…”

Section: Discussionmentioning

confidence: 97%

“…). This model could achieve highest ROC curve because of better selection of features, dealing with imbalance data or overfitting through smote analysis was the main key for the best prediction (5).…”

Section: Figure 2 Roc Curvementioning

confidence: 99%

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Using Machine Learning Algorithms to predict sepsis and its stages in ICU patients

Ghias

Haq²,

Arshad

et al. 2022

Preprint

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Sepsis is blood poisoning disease that occurs when body shows dysregulated host response to an infection and cause organ failure or tissue damage which may increase the mortality rate in ICU patients. As it becomes major health problem , the hospital cost for treatment of sepsis is increasing every year. Different methods have been developed to monitor sepsis electronically, but it is necessary to predict sepsis as soon as possible before clinical reports or traditional methods, because delayed in treatment can increase the risk of mortality with every single hour. For the early detection of sepsis, specifically in ICU patients , different machine learning models i.e Linear learner, Multilayer perceptron neural networks, Random Forest, lightgbm and Xgboost has trained on the data set proposed by Physio Net/Computing in Cardiology Challenge in 2019. This study shows that Machine learning algorithms can accurately predict sepsis at the admission time of patient in ICU by using six vitals signs extracted from patient records over the age of 18 years. After comparative analysis of machine learning models , Xgboost model achieved a highest accuracy of 0.98 , precision of 0.97, and recall 0.98 under the precision-recall curve on the publicly available data. Early prediction of sepsis can help clinicians to implement supportive treatments and reduce the mortality rate as well as healthcare expenses

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Section: Discussionmentioning

confidence: 74%

Section: Discussionmentioning

confidence: 97%

See 1 more Smart Citation

Using Machine Learning Algorithms to predict sepsis and its stages in ICU patients

Ghias

Haq²,

Arshad

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Fu et al [8] proposed an ensemble model combining three different predictive models which are LightGBM, XGBoost, and Random Forest (RF). Dataset unbalancing was handled by removing outliers, and missing values were handled using the "pad" method, called carry-forward, where the missing value was replaced by previous non-missing value.…”

Section: Machine Learning Models For Sepsis Predictionmentioning

confidence: 99%

Comparative Study of Machine Learning Models for Onset Sepsis Prediction

Rayan

Alfonse

Salem

2021

Digital Transformation Technology

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Sepsis is one of life-threatening diseases that is caused by unbalanced body response to some chemicals that the body release into its blood stream when fighting an infection. Early prediction of sepsis can decrease mortality rates. Machine learning techniques can improve the accuracy of early sepsis prediction. This paper presents a comparative study for recent machine learning models that can be used for sepsis prediction using datasets provided by international Challenge, named PhysioNet/Computing in Cardiology 2019.

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“…LSTM predicts the output for future time steps based on past data. Schellenberger et al (Fu et al, 2019) proposed an ensemble LSTM model for performing clinical sepsis detection. The aim was to develop an automated detection tool for detecting sepsis 6 h prior to the prediction made to clinics with better accuracy.…”

Section: Related Workmentioning

confidence: 99%

Early prediction and monitoring of sepsis using sequential long short term memory model

et al. 2021

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Sepsis is a severe life-threatening disease, which is the body's extreme reverberation to any virus leading to failed organs, damaged tissue, or death. It requires accurate and efficient real-time detection. Continuous and potent monitoring of patient health data can be useful in predicting the potential risks the patient might be exposed to, based on their recent history of medical records. Machine learning models have proven to be a significant approach in performing accurate and precise predictions, especially in the medical field. In this paper, a smart network with a long short term memory (LSTM) based model at its heart is proposed for early prediction of Sepsis for patients admitted in the ICU. The network operates on time series data and predicts the probability of a patient developing sepsis based on the historical medical data of the patient. It comprises Internet of Things based devices which have proven to be impactful in the areas of acquiring continuous and real-time data in the healthcare field. In this paper, an architecture for early prediction and monitoring Sepsis with minimized latency through LSTM network is proposed by designing a decentralized prediction model in a fog-based environment. The model had an accuracy of 95.1% after the last epoch with validation accuracy as 95%. The receiver operating characteristic curve area was reported as 0.864 for testing data with an accuracy of 95.1%. The model was not over-fitted since the validation accuracy was significantly close to the training accuracy of the model.

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An Ensemble Machine Learning Model for the Early Detection of Sepsis from Clinical Data

Cited by 13 publications

References 12 publications

Using Machine Learning Algorithms to predict sepsis and its stages in ICU patients

Using Machine Learning Algorithms to predict sepsis and its stages in ICU patients

Comparative Study of Machine Learning Models for Onset Sepsis Prediction

Early prediction and monitoring of sepsis using sequential long short term memory model

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