E-Health Ultrasonic Diagnostic Monitoring for Analysis of Cardiac Insufficiency and Neuronal Regulation in Patients with Sepsis in Emergency Department under Image Reconstruction Algorithm

Fang, Zhonghua; Mao, Zhang

doi:10.1155/2022/9415694

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Fang et al discussed a method for e-health ultrasonic diagnostic system for cardiac insufficiency and neuronal regulation in patients with sepsis using an image reconstruction algorithm [ 13 ]. Shashikumar et al discussed a method called DeepAISE, which defines a model for early prediction of sepsis using an interpretable and recurrent neural survival [ 14 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning Model for Early Prediction and Detection of Sepsis in Intensive Care Unit Patients

Singh

Singh²,

Khan³

et al. 2022

Journal of Healthcare Engineering

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In today’s scenario, sepsis is impacting millions of patients in the intensive care unit due to the fact that the mortality rate is increased exponentially and has become a major challenge in the field of healthcare. Such peoples require determinant care which increases the cost of the treatment by using a large number of resources because of the nonavailability of the resources. The treatment of sepsis is available in the early state, but treatment is not started at the right time, and then it converts to the advanced level of sepsis and increases the fatalities. Thus, an intensive analysis is required to detect and identify sepsis at the early stage. There are some models available that work based on the manual score and based on only the biomark features, but these are not fully automated. Some machine learning-based models are also available, which can reduce the mortality rate, but accuracy is not up to date. This paper proposes a machine learning model for early detecting and predicting sepsis in intensive care unit patients. Various models, random forest (RF), linear regression (LR), support vector machine (SVM), naive Bayes (NB), ensemble (of SVM, RF, NB, and LR), XGBoost, and proposed ensemble (of SVM, RF, NB, LR, and XGBoost), are simulated by using the collected data from intensive care unit patient’s database that is based on the clinical laboratory values and vital signs. The performance of the models is evaluated by considering the same datasets. The balanced accuracy of RF, LR, SVM, NB, ensemble (of SVM, RF, NB, and LR), XGBoost, and proposed ensemble (of SVM, RF, NB, LR, and XGBoost) is 0.90, 0.73, 0.93, 0.74, 0.94, 0.95, and 0.96, respectively. It is also evident from the experimental results that the proposed ensemble model performs well as compared to the other models.

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Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning Model for Early Prediction and Detection of Sepsis in Intensive Care Unit Patients

Singh

Singh²,

Khan³

et al. 2022

Journal of Healthcare Engineering

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show abstract

“…This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”

mentioning

confidence: 99%

Retracted: E‐Health Ultrasonic Diagnostic Monitoring for Analysis of Cardiac Insufficiency and Neuronal Regulation in Patients with Sepsis in Emergency Department under Image Reconstruction Algorithm

Methods in Medicine

2023

Computational and Mathematical Methods in Medicine

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E-Health Ultrasonic Diagnostic Monitoring for Analysis of Cardiac Insufficiency and Neuronal Regulation in Patients with Sepsis in Emergency Department under Image Reconstruction Algorithm

Cited by 2 publications

References 17 publications

A Machine Learning Model for Early Prediction and Detection of Sepsis in Intensive Care Unit Patients

A Machine Learning Model for Early Prediction and Detection of Sepsis in Intensive Care Unit Patients

Retracted: E‐Health Ultrasonic Diagnostic Monitoring for Analysis of Cardiac Insufficiency and Neuronal Regulation in Patients with Sepsis in Emergency Department under Image Reconstruction Algorithm

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