Mortality prediction of rats in acute hemorrhagic shock using machine learning techniques.
This study sought to determine a mortality prediction model that could be used for triage in the setting of acute hemorrhage from trauma. To achieve this aim, various machine learning techniques were applied using the rat model in acute hemorrhage. Thirty-six anesthetized rats were randomized into t...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 51; no. 9; pp. 1059 - 1068 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Sep2013
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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=104085644&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104085644 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2013 vid: 51 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104085644 NLM23793529 2012212870 10.1007/s11517-013-1091-0 NLM23793529 104085644 ppf: 1059 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Mortality prediction of rats in acute hemorrhagic shock using machine learning techniques. aug: au: Kim, Kyung-Ah Choi, Joon Yul Yoo, Tae Keun Kim, Sung Kean Chung, Kilsoo Kim, Deok Won affil: Department of Biomedical Engineering, Chungbuk National University, College of Medicine, Cheongju, Korea. sug: subj: Artificial Intelligence Models, Statistical Shock, Hemorrhagic Physiopathology Algorithms Animal Studies Body Temperature Hemodynamics Physiology Lactic Acid Blood Logistic Regression Male Nonparametric Statistics Random Assignment Rats Respiratory Rate Physiology ROC Curve Male ab: This study sought to determine a mortality prediction model that could be used for triage in the setting of acute hemorrhage from trauma. To achieve this aim, various machine learning techniques were applied using the rat model in acute hemorrhage. Thirty-six anesthetized rats were randomized into three groups according to the volume of controlled blood loss. Measurements included heart rate (HR), systolic and diastolic blood pressures (SBP and DBP), mean arterial pressure, pulse pressure, respiratory rate, temperature, blood lactate concentration (LC), peripheral perfusion (PP), shock index (SI, SI = HR/SBP), and a new hemorrhage-induced severity index (NI, NI = LC/PP). NI was suggested as one of the good candidates for mortality prediction variable in our previous study. We constructed mortality prediction models with logistic regression (LR), artificial neural networks (ANN), random forest (RF), and support vector machines (SVM) with variable selection. The SVM model showed better sensitivity (1.000) and area under curve (0.972) than the LR, ANN, and RF models for mortality prediction. The important variables selected by the SVM were NI and LC. The SVM model may be very helpful to first responders who need to make accurate triage decisions and rapidly treat hemorrhagic patients in cases of trauma. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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