Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies.
Subarachnoid hemorrhage (SAH) is one of the major health issues known to society and has a higher mortality rate. The clinical factors with computed tomography (CT), magnetic resonance image (MRI), and electroencephalography (EEG) data were used to evaluate the performance of the developed method. I...
| Publicado en: | BioMed Research International pp. 1 - 13 |
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| Autores principales: | , |
| Formato: | diagnostic images research systematic review tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
1/27/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154923332&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154923332 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 1/27/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 154923332 154923332 154923332 10.1155/2022/5416726 154923332 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies. aug: au: Sengupta, Jewel Alzbutas, Robertas affil: Kaunas University of Technology, K. Donelaičio g. 73, Kaunas 44249, Lithuania sug: subj: Risk Assessment Subarachnoid Hemorrhage Risk Factors Subarachnoid Hemorrhage Mortality Prediction Models Human Systematic Review Tomography, X-Ray Computed Utilization Magnetic Resonance Imaging Utilization Electroencephalography Utilization Machine Learning Deep Learning Methods Subarachnoid Hemorrhage Diagnosis Neural Networks (Computer) Support Vector Machine ab: Subarachnoid hemorrhage (SAH) is one of the major health issues known to society and has a higher mortality rate. The clinical factors with computed tomography (CT), magnetic resonance image (MRI), and electroencephalography (EEG) data were used to evaluate the performance of the developed method. In this paper, various methods such as statistical analysis, logistic regression, machine learning, and deep learning methods were used in the prediction and detection of SAH which are reviewed. The advantages and limitations of SAH prediction and risk assessment methods are also being reviewed. Most of the existing methods were evaluated on the collected dataset for the SAH prediction. In some researches, deep learning methods were applied, which resulted in higher performance in the prediction process. EEG data were applied in the existing methods for the prediction process, and these methods demonstrated higher performance. However, the existing methods have the limitations of overfitting problems, imbalance data problems, and lower efficiency in feature analysis. The artificial neural network (ANN) and support vector machine (SVM) methods have been applied for the prediction process, and considerably higher performance is achieved by using this method. pubtype: Academic Journal doctype: diagnostic images research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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