Artificial neural networks: Predicting head CT findings in elderly patients presenting with minor head injury after a fall.
Objectives: To construct an artificial neural network (ANN) model that can predict the presence of acute CT findings with both high sensitivity and high specificity when applied to the population of patients≥age 65years who have incurred minor head injury after a fall.Methods: An ANN was created in...
| Publicado en: | American Journal of Emergency Medicine Vol. 35; no. 2; pp. 260 - 268 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Elsevier B.V.
Feb2017
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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=121451569&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121451569 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07356757 1X2 jtl: American Journal of Emergency Medicine issn: 07356757 maglogo: N pubinfo: dt: Feb2017 vid: 35 iid: 2 pid: 82545 pub: Elsevier B.V. place: Philadelphia, Pennsylvania artinfo: ui: 121451569 121451569 NLM27876174 10.1016/j.ajem.2016.10.065 NLM27876174 121451569 ppf: 260 ppct: 8 formats: tig: atl: Artificial neural networks: Predicting head CT findings in elderly patients presenting with minor head injury after a fall. aug: au: Dusenberry, Michael W. Brown, Charles K. Brewer, Kori L. affil: Brody School of Medicine, East Carolina University, 600 Moye Blvd, Greenville, NC 27834, USA sug: subj: Accidental Falls Tomography, X-Ray Computed Standards Trauma Centers Standards Neural Networks (Computer) Head Injuries Retrospective Design Trauma Centers Statistics and Numerical Data United States Forecasting Tomography, X-Ray Computed Adverse Effects Head Injuries Economics Head Injuries Etiology Tomography, X-Ray Computed Economics Male Female Aged Decision Support Systems, Clinical Regression Cost Benefit Analysis Trauma Centers Economics Scales Aged: 65+ years Male Female ab: Objectives: To construct an artificial neural network (ANN) model that can predict the presence of acute CT findings with both high sensitivity and high specificity when applied to the population of patients≥age 65years who have incurred minor head injury after a fall.Methods: An ANN was created in the Python programming language using a population of 514 patients ≥ age 65 years presenting to the ED with minor head injury after a fall. The patient dataset was divided into three parts: 60% for "training", 20% for "cross validation", and 20% for "testing". Sensitivity, specificity, positive and negative predictive values, and accuracy were determined by comparing the model's predictions to the actual correct answers for each patient.Results: On the "cross validation" data, the model attained a sensitivity ("recall") of 100.00%, specificity of 78.95%, PPV ("precision") of 78.95%, NPV of 100.00%, and accuracy of 88.24% in detecting the presence of positive head CTs. On the "test" data, the model attained a sensitivity of 97.78%, specificity of 89.47%, PPV of 88.00%, NPV of 98.08%, and accuracy of 93.14% in detecting the presence of positive head CTs.Conclusions: ANNs show great potential for predicting CT findings in the population of patients ≥ 65 years of age presenting with minor head injury after a fall. As a good first step, the ANN showed comparable sensitivity, predictive values, and accuracy, with a much higher specificity than the existing decision rules in clinical usage for predicting head CTs with acute intracranial findings. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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