Development of two artificial neural network models to support the diagnosis of pulmonary tuberculosis in hospitalized patients in Rio de Janeiro, Brazil.
Pulmonary tuberculosis (PTB) remains a worldwide public health problem. Diagnostic algorithms to identify the best combination of diagnostic tests for PTB in each setting are needed for resource optimization. We developed one artificial neural network model for classification (multilayer perceptron-...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 54; no. 11; pp. 1751 - 1760 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2016
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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=118887916&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 118887916 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2016 vid: 54 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 118887916 118887916 NLM27016365 10.1007/s11517-016-1465-1 NLM27016365 118887916 ppf: 1751 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Development of two artificial neural network models to support the diagnosis of pulmonary tuberculosis in hospitalized patients in Rio de Janeiro, Brazil. aug: au: Aguiar, Fábio Torres, Rodrigo Pinto, João Kritski, Afrânio Seixas, José Mello, Fernanda Aguiar, Fábio S Torres, Rodrigo C Pinto, João V F Kritski, Afrânio L Seixas, José M Mello, Fernanda C Q affil: Instituto de Doenças do Tórax (IDT)/Clementino Fraga Filho Hospital (CFFH) , Federal University of Rio de Janeiro , Rua Professor Rodolpho Paulo Rocco, n° 255 - 6° Andar - Cidade Universitária - Ilha do Fundão 21941-913 Rio De Janeiro Brazil sug: subj: Models, Biological Hospitalization Neural Networks (Computer) Tuberculosis, Pulmonary Diagnosis Male Adult Predictive Value of Tests Female Middle Age Brazil Sensitivity and Specificity Scales Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Pulmonary tuberculosis (PTB) remains a worldwide public health problem. Diagnostic algorithms to identify the best combination of diagnostic tests for PTB in each setting are needed for resource optimization. We developed one artificial neural network model for classification (multilayer perceptron-MLP) and another risk group assignment (self-organizing map-SOM) for PTB in hospitalized patients in a high complexity hospital in Rio de Janeiro City, using clinical and radiologic data collected from 315 presumed PTB cases admitted to isolation rooms from March 2003 to December 2004 (TB prevalence = 21.5 %). The MLP model included 7 variables-radiologic classification, age, gender, cough, night sweats, weight loss and anorexia. The sensitivity of the MLP model was 96.0 % (95 % CI ±2.0), the specificity was 89.0 % (95 % CI ±2.0), the positive predictive value was 72.5 % (95 % CI ±3.5) and the negative predictive value was 98.5 % (95 % CI ±0.5). The variable with the highest discriminative power was the radiologic classification. The high negative predictive value found in the MLP model suggests that the use of this model at the moment of hospital admission is safe. SOM model was able to correctly assign high-, medium- and low-risk groups to patients. If prospective validation in other series is confirmed, these models can become a tool for decision-making in tertiary health facilities in countries with limited resources. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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