The use of fuzzy backpropagation neural networks for the early diagnosis of hypoxic ischemic encephalopathy in newborns.
OBJECTIVE: To establish an early diagnostic system for hypoxic ischemic encephalopathy (HIE) in newborns based on artificial neural networks and to determine its feasibility. METHODS: Based on published research as well as preliminary studies in our laboratory, multiple noninvasive indicators with h...
| Publicado en: | Journal of Biomedicine & Biotechnology pp. 349490 - 349491 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2011
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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=104531691&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104531691 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11107243 137K jtl: Journal of Biomedicine & Biotechnology issn: 11107243 maglogo: N pubinfo: dt: 2011 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104531691 104531691 2011457102 NLM21811381 PMC3147002 104531691 ppf: 349490 ppct: 1 formats: fmt: @attributes: type: P tig: atl: The use of fuzzy backpropagation neural networks for the early diagnosis of hypoxic ischemic encephalopathy in newborns. aug: au: Li, Liu Liqing, Huo Hongru, Lu Feng, Zhang Chongxun, Zheng Pokhrel, Shami Jie, Zhang affil: Department of Neonatology, Hospital of the Medical College of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China. nellie918@yahoo.com.cn sug: subj: Logic Hypoxia-Ischemia, Brain Diagnosis Neural Networks (Computer) Information Science Methods Algorithms Diagnosis Early Diagnosis Health Status Indicators Infant, Newborn Reproducibility of Results Sensitivity and Specificity Infant, Newborn: birth-1 month ab: OBJECTIVE: To establish an early diagnostic system for hypoxic ischemic encephalopathy (HIE) in newborns based on artificial neural networks and to determine its feasibility. METHODS: Based on published research as well as preliminary studies in our laboratory, multiple noninvasive indicators with high sensitivity and specificity were selected for the early diagnosis of HIE and employed in the present study, which incorporates fuzzy logic with artificial neural networks. RESULTS: The analysis of the diagnostic results from the fuzzy neural network experiments with 140 cases of HIE showed a correct recognition rate of 100% in all training samples and a correct recognition rate of 95% in all the test samples, indicating a misdiagnosis rate of 5%. CONCLUSION: A preliminary model using fuzzy backpropagation neural networks based on a composite index of clinical indicators was established and its accuracy for the early diagnosis of HIE was validated. Therefore, this method provides a convenient tool for the early clinical diagnosis of HIE. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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